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machine language

Definition of machine language

called also machine code

Examples of machine language in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'machine language.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1947, in the meaning defined at sense 1

Dictionary Entries Near machine language

machine-hour

machine learning

Cite this Entry

“Machine language.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/machine%20language. Accessed 11 Nov. 2023.

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Machine language

Sometimes called machine code or object code , machine language is a collection of binary digits or bits that the computer reads and interprets. Machine language is the only language a computer can understand.

The exact machine language for a program or action can differ by the operating system . The specific operating system dictates how a compiler writes a program or action into machine language.

Computer programs are written in one or more programming languages , like C++ , Java , or Visual Basic . A computer cannot directly understand the programming languages used to create computer programs, so the program code must be compiled . Once a program's code is compiled, the computer can understand it because the program's code is turned into machine language.

Machine language example

Below is an example of machine language (binary) for the text "Hello World."

Below is another example of machine language (non-binary), which prints the letter "A" 1000 times to the computer screen.

Related information

• How does a computer convert text into binary or 0's and 1's?
• How does a computer process data into information?

Assembly language , Binary , Compilation , High-level language , Low-level language , Machine , Machine-readable , ML , Object file , Operation code , Programming terms , Pseudolanguage , Special purpose language

Machine Language

Machine language, or machine code, is a low-level language comprised of binary digits (ones and zeros). High-level languages , such as Swift and C++ must be compiled into machine language before the code is run on a computer.

Since computers are digital devices, they only recognize binary data. Every program, video, image, and character of text is represented in binary. This binary data , or machine code, is processed as input by the CPU . The resulting output is sent to the operating system or an application , which displays the data visually. For example, the ASCII value for the letter "A" is 01000001 in machine code, but this data is displayed as "A" on the screen. An image may have thousands or even millions of binary values that determine the color of each pixel .

While machine code is comprised of 1s and 0s, different processor architectures use different machine code. For example, a PowerPC processor, which has a RISC architecture, requires different code than an Intel x86 processor, which has a CISC architecture. A compiler must compile high-level source code for the correct processor architecture in order for a program to run correctly.

Machine Language vs Assembly Language

Machine language and assembly language are both low-level languages, but machine code is below assembly in the hierarchy of computer languages. Assembly language includes human-readable commands, such as mov , add , and sub , while machine language does not contain any words or even letters. Some developers manually write assembly language to optimize a program, but they do not write machine code. Only developers who write software compilers need to worry about machine language.

NOTE: While machine code is technically comprised of binary data, it may also be represented in hexadecimal values. For example, the letter "Z," which is 01011010 in binary, may be displayed as 5A in hexadecimal code.

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Related terms.

• Programming Language
• Assembly Language
• Source Code
• Hexadecimal

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Meaning of machine language in English

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What is Machine Language?

Of all universal programming languages, machine language is the most basic. Computers can’t understand any communication systems other than hardware-dependent machine code. And this machine code consists of numerical instructions, including:

• Hexadecimal

You can also refer to machine language as machine code or object code. It is a collection of binary digits (or bits) that the computer interprets.

If you took a computer science class, you have likely heard about machine language. But did you ever wonder, “Professor, what do you mean by machine language? What is machine language in computer programming?” If so, read on to find out more.

Machine Code Execution

Every family of processors has its own set of machine instructions. Machine language programming is what lays these instructions out. Every component that makes the machine function has its own arrangement of units. These units represent a machine’s actionable items. And they are all a binary nature, with values of 1 or 0. They operate using binary code. This is a basic or low level programming language.

The instruction sets of all machine code configurations align with similar processor classes. The structural configuration of any processor class matches its unique instruction set.

Set Length Diversity and Uniformity

Arrangements within different machine language instructions vary. Some sets have instructions that share identical lengths with one another. Other instruction sets have machine instructions with varying lengths. The instruction length of a set depends on the processor’s construction.  And this depends on whatever operation code is in place.

An operation code, or an opstring, determines the operation the assembly code will carry out. The opstring also determines the exact category of an instruction set. That regulates the length of the instructions within.

Machine Code’s Margin of Error

By nature, machine code has many small numerical components. Composing any program written through machine language alone is difficult. To do this, a programmer’s numerical address calculations must be airtight.

So, machine code’s margin of error is large. Because of this, it is rare for programs to be in machine dependent binary code alone. The answer to this problem is assembly language.

What is Machine Language and Assembly Language?

Machine language in computer programming is a basic numerical code. It is more than binary code. Assembly languages are different. Assembly language is a low-level symbolic code. Low level language is a human-readable programming language. It communicates with a computer’s hardware.

Assembly language has many uses, like:

• Accessing specialized processor instructions
• Evaluating critical performance errors
• Manipulating hardware

Assembly language is also faster than high level programming languages. This is crucial for time-sensitive activities.

How Is Assembly Language Different from Machine Language?

There are many similar aspects of assembly and machine language. But you could argue that these languages have more differences than similarities. A machine language program and assembly language have different:

Below, we explain these differences between assembly and machine code. We also discuss the difference between a low level programming language and a high level programming language.

Modifications and error fixing is impossible in machine language. But assembly languages allow programmers to:

• Change machine code
• Work with programming languages

Assembly language is also easier to understand and memorize. But assembly language isn’t perfect. It is slower than machine code. To understand its benefits and to create machine understandable form, we can look at its origin.

Using binary numbers to represent computer instructions originated in the 1930s and 40s. It came from the minds of computer geniuses John Mauchly and John Atanasoff. But no single person invented machine language. It evolved over time.

The first machine language-programmed computers appeared in the mid-20 th century. Before that, people operated computers with:

They were inefficient and hard to operate. They were such a nuisance that scientists began to develop machine language. Binary code made things much simpler. And it allowed computer to work on complicated problems in record time.

Unlike machine language, the credit for inventing assembly language goes to specific people. In 1947, Kathleen Booth began theoretical work on the concept.

In 1948, the Electronic Delay Storage Automatic Calculator had an assembler. It used mnemonics by David Wheeler, the creator of the first physical assembler. Mnemonics are devices that help you remember something. These devices can include:

• Patterns of letters or words
• Rhymes/songs

In 1955, Stan Poley wrote an assembly language called Symbolic Optimal Assembly Program. Assembly language erased the slow and error-prone tendencies of early coding. It became the standard for many programming types.

Readability

Machine language is unreadable to humans. Trying to read machine language is like trying to read genetic code. Impossible. Even the US Copyright Office cannot identify whether machine coded programs are original. It is the only language read by computers alone. It is a low-level programming language.

The machine language consists of ones and zeros. The assembly language’s syntax is more like the English language’s syntax. This makes it easier for humans to understand. Assembly language uses mnemonics and symbols to signify instructions.

The mnemonic signifies a machine language instruction. For instance, INP represents input and OUT represents output. These instructions consist of an operation (or opcode) and one or more operands. Operands are data addresses, registers, or values.

Machine language is any low-level object code that controls the central processing unit (CPU). Its assembly language instructions help the CPU perform operations like:

• Arithmetic logic unit

These tasks change the data in the CPU’s memory.

Machine language is numerical. It is the lowest-level CPU interface that programmers can access. Programmers translate machine code into readable strings using assembly language. They use a translator program to do this. And assembly has many more applications than machine language. Humans use assembly language for:

• Air-conditioning control systems
• Automobile ignition
• Firmware for cellphones
• Fuel operating systems
• Security systems

Assembly language is also useful for machine language programs happening in real time. These programs need their programmers to maintain control from end to end. Examples include:

• Flight navigation systems
• Medical equipment
• Simulations

In programming, assembly language is also useful for:

• Decryption/encryption algorithms
• Low level language and code
• Writing code that interacts with hardware

In general, assembly language has more uses than machine code. More than anything, it helps humans understand machine code. You can convert assembly language to code. But you can’t understand a machine’s binary without assembly.

Best Majors for Studying Machine Language

Now that you know the basics of machine language, you might wish to learn more. In higher education, some majors address machine language more than others. Here is a list of the best majors on machine language. If you want to understand a low level language or high level language, you may want to major in one of these areas.

Computer Programming

Computer programming majors learn to write code for computer software and applications. Machine language is a crucial part of this program. You will learn machine code and programming languages like:

You will also study high level programming languages. A high level language is different other machine languages. You will learn the difference between machine code written for low level language and high level language.

There are many components to this major. You don’t focus on only language. On top of machine language, expect to take courses in:

• Computer networking
• Database design

At the end of your degree program, you will complete a capstone project. This final project teaches you to solve real-world programming issues. You will use the skills you learned to solve a software problem. Examples of capstone projects include:

• Create an app to track medications
• Create computer programs for business
• Design an online survey system
• Program a computer game
• Program a finance manager

Undergraduate degrees in computer programming provide you many skills. But that doesn’t have to be the final step in your education career. Computer programming graduate degrees are also available. A graduate degree can prove you as an expect in high level programming languages.

Computer Science

Computer science (CS) and computer programming are similar majors. Both include learning about machine language. You learn the difference between a low level programming language and high level programming language. You also study middle level language. But the two majors have some key differences.

Computer programming focuses on coding and software development. Computer science is broader. It has a large range of career paths, including:

• Artificial intelligence
• Data science

In general, computer science is the theory of computer processes. Computer programming is the practical application of this theory. So, for a more rounded approach to learning, choose computer science. CS students study subjects like:

• Bioinformatics
• Computer systems and networks
• Database systems
• High level programming languages
• Human-computer interaction
• Numerical analysis
• Programming languages
• Software engineering

Programming language is crucial to learning computer science. But it isn’t everything. There is much more to computer processes than coding.

Data Science

In data science programs, students learn many useful skills, including:

• Computer programming language
• Data analysis
• Machine language
• Middle level language
• Predictive modeling

Students learn to gather information and present their findings in concise formats. This makes communicating their data-driven recommendations easier. Future colleagues will appreciate this.

Data science is the foundation of topics like:

• Deep learning
• Exact machine language
• Machine learning

This major gives students many career options. Potential employers include more than big tech companies like Apple or Google. They can also work in a variety of fields, including:

• Telecommunications

Some of the most in-demand careers for data science majors are:

• Applications architect
• Data engineer
• Machine learning engineer

Information Technology

Information technology (IT) majors study general subjects like:

• Communications
• Computer science

You may also choose to specialize in subjects like:

• Digital communications
• Game development
• Programming languages (high level language, middle level language, low level programming language)

No matter what focus you choose, you will gain strong communication and tech skills. IT students study how computers support business, communications, and research. You will learn everything from computer hardware to human-computer relationships.

Outside of general IT classes, students may also:

• Intern at a tech company
• Learn from diverse perspectives
• Study the social impacts of IT
• Study the ethical issues of IT
• Take classes in other departments like business or philosophy

Information technology students are more than “tech people.” They are critical thinkers and excellent communicators. IT majors are flexible problem solvers with a passion for information.

Yes, information technology students must be comfortable with computers. But they also need to work well with others.

Mathematics

Math majors study a variety of topics, including:

• Number theory

Within these subjects, math majors study forms, quantities, and symbolic logic. You can also choose to specialize in computer science subjects. This will allow you to study machine language, such as high level languages and low level language. Many math graduates find knowing high level languages helps them land computer science careers.

On top of classes, math students can also:

• Compete in math competitions
• Prepare assembly language statements
• Study abstract and complicated topics
• Take classes in computer labs
• Write papers on mathematical concepts
• Write papers on research projects

Math majors tend to enjoy puzzles and challenges. They identify and explore the world’s patterns. Because of these great skills, math majors have many career options. Examples of possible career paths for math students include:

• Computer programmer
• Financial analyst
• Software developer
• Statistician

This may not be an obvious answer for aspiring programmers. You might not study machine languages. But mathematics is a great major for tech people, or those who want to know more about assembly languages.

Software Engineering

Software engineering evolves at continuous rates. As a software engineering major, you will:

• Study programming languages like machine code
• Study the scientific and mathematical basis of computers
• Learn how to analyze and design computer software

Software engineering covers everything about machine language. Machine language is the basis for software engineering. Common courses for this major include:

• Computer systems fundamentals
• Design and analysis of algorithms
• Intro to high level programming language and low level programming language
• Introduction to programming languages and machine code
• Principles of database management

Software engineering majors do more than take classes. They are good problem solvers and critical thinkers. These skills allow them to:

• Design software systems of their own
• Help develop high level programming languages
• Intern at software companies
• Solve complex issues in existing software systems

Software engineering students also complete final projects before graduation. The project will involve working in teams. You and your classmates will use what you learned to develop software together.

It’s best to choose a project that mirrors your future career. Some colleges have outside companies sponsor student projects. This lets you work on real-world problems. Some examples of capstone projects include:

• Creating an app for women’s safety
• Designing a virtual classroom
• Designing an online election system
• Designing online survey systems
• Programming a smart security surveillance system

Try to come up with a unique idea for your capstone project. And remember, you don’t need to do it alone. Your team can brainstorm ideas together. Test your knowledge and show professors your mastery of software engineering and machine languages.

Machine language is the foundation for all programmable executions. But it also has its cons. Before, programmers had to accept the difficulties of managing many numerical values. But modern assembly language streamlines this process now.

Still, learning about machine language is crucial. And there are many routes to studying it. You may major in a wider array of subjects within the computer world. Each will touch on the foundation that is machine language.

But there is more to computer systems than coding. This industry values both tech skills and well-roundedness. You will need a strong business acumen and communication skills. Most of all, you will immortalize information through computers.

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Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of  artificial intelligence or AI —concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.

NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots , and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure—these just a few of the irregularities of human language that take humans years to learn, but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful.

Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting. Some of these tasks include the following:

• Speech recognition , also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
• Part of speech tagging , also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
• Word sense disambiguation is the selection of the meaning of a word with multiple meanings  through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
• Named entity recognition,  or NEM, identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
• Co-reference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (e.g., ‘she’ = ‘Mary’),  but it can also involve identifying a metaphor or an idiom in the text  (e.g., an instance in which 'bear' isn't an animal but a large hairy person).
• Sentiment analysis  attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
• Natural language generation  is sometimes described as the opposite of speech recognition or speech-to-text; it's the task of putting structured information into human language.

See the blog post “ NLP vs. NLU vs. NLG: the differences between three natural language processing concepts ” for a deeper look into how these concepts relate.

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Python and the Natural Language Toolkit (NLTK)

The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs.

The NLTK includes libraries for many of the NLP tasks listed above, plus libraries for subtasks, such as sentence parsing, word segmentation, stemming and lemmatization (methods of trimming words down to their roots), and tokenization (for breaking phrases, sentences, paragraphs and passages into tokens that help the computer better understand the text). It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text.

Statistical NLP, machine learning, and deep learning

The earliest NLP applications were hand-coded, rules-based systems that could perform certain NLP tasks, but couldn't easily scale to accommodate a seemingly endless stream of exceptions or the increasing volumes of text and voice data.

Enter statistical NLP, which combines computer algorithms with machine learning and deep learning models to automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements. Today, deep learning models and learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) enable NLP systems that 'learn' as they work and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabeled text and voice data sets. 

For a deeper dive into the nuances between these technologies and their learning approaches, see “ AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference? ”

Natural language processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:

• Spam detection:  You may not think of spam detection as an NLP solution, but the best spam detection technologies use NLP's text classification capabilities to scan emails for language that often indicates spam or phishing. These indicators can include overuse of financial terms, characteristic bad grammar, threatening language, inappropriate urgency, misspelled company names, and more. Spam detection is one of a handful of NLP problems that experts consider 'mostly solved' (although you may argue that this doesn’t match your email experience).
• Machine translation:  Google Translate is an example of widely available NLP technology at work. Truly useful machine translation involves more than replacing words in one language with words of another.  Effective translation has to capture accurately the meaning and tone of the input language and translate it to text with the same meaning and desired impact in the output language. Machine translation tools are making good progress in terms of accuracy. A great way to test any machine translation tool is to translate text to one language and then back to the original. An oft-cited classic example: Not long ago, translating “ The spirit is willing but the flesh is weak” from English to Russian and back yielded “ The vodka is good but the meat is rotten .” Today, the result is “ The spirit desires, but the flesh is weak ,” which isn’t perfect, but inspires much more confidence in the English-to-Russian translation.
• Virtual agents and chatbots:   Virtual agents such as Apple's Siri and Amazon's Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate action or helpful comments. Chatbots perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to our questions—anticipated or not—with relevant and helpful answers in their own words.
• Social media sentiment analysis: NLP has become an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze language used in social media posts, responses, reviews, and more to extract attitudes and emotions in response to products, promotions, and events–information companies can use in product designs, advertising campaigns, and more.
• Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or busy readers who don't have time to read full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add useful context and conclusions to summaries.

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Why the Abortion Ballot Question in Ohio Is Confusing Voters

Ballot questions have been a winning strategy for abortion rights, even in red states. But complicated ballot language and misinformation have some abortion rights supporters worried.

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By Kate Zernike and Lisa Lerer

Kate Zernike reported from Ohio, and Lisa Lerer from New York.

Follow our live updates from Election Day 2023 .

Volunteers canvassing in favor of a ballot initiative to establish a constitutional right to abortion stopped Alex Woodward at a market hall in Ohio to ask if they could expect her vote in November.

Ms. Woodward said she favors abortion rights and affirmed her support. But as the canvassers moved on through the hall, she realized she was not sure how to actually mark her ballot. “I think it’s a yes,” she said. “Maybe it’s a no?”

Anyone in Ohio could be forgiven some confusion — the result of an avalanche of messaging and counter-messaging, misinformation and complicated language around what the amendment would do, and even an entirely separate ballot measure with the same name just three months ago. All this has abortion rights supporters worried in an off-year election race that has become the country’s most watched.

Ohio Issue 1 Election Results: Establish a Constitutional Right to Abortion

See full results and maps from the 2023 Ohio elections.

Across the country, abortion rights groups have been on an unexpected winning streak with ballot measures that put the question of abortion straight to voters. They have prevailed in six out of six since the Supreme Court overturned Roe v. Wade last year, even in red states like Kansas.

But the measure in Ohio is their toughest fight yet. It is the first time that voters in a red state are being asked to affirmatively vote “yes” to a constitutional amendment establishing a right to abortion, rather than “no” to preserve the status quo established by courts. Ohio voters have historically tended to reject ballot amendments.

Republicans who control the levers of state power have used their positions to try to influence the vote, first by calling a special election in August to try to raise the threshold for passing ballot amendments, then when that failed, by using language favored by anti-abortion groups to describe the amendment on the ballot and in official state communications.

Anti-abortion groups, which were caught flat-footed against the wave of voter anger that immediately followed the court overturning Roe, have had more time to sharpen their message. They have stoked fears about loss of parental rights and allowing children to get transition surgeries, even though the proposed amendment mentions neither.

Democrats nationally are watching to see if the outrage that brought new voters to the party last year maintains enough momentum to help them win even in red states in the presidential and congressional races in 2024. And with abortion rights groups pushing similar measures on ballots in red and purple states next year, anti-abortion groups are hoping they have found a winning strategy to stop them.

“Certainly, we know that all eyes are on Ohio right now,” said Amy Natoce, the spokeswoman for Protect Women Ohio, a group founded by national anti-abortion groups including Susan B. Anthony Pro-Life America to oppose the amendment.

With early voting underway since mid-October, the state is a frenzy of television and social media ads, multiple rallies a day and doorknobs laden with campaign literature, with each side accusing the other of being too extreme for Ohio.

A “yes” on Issue 1, a citizen-sponsored ballot initiative pushed largely by doctors, would amend the state’s constitution to establish a right to “carry out one’s own reproductive decisions,” including on abortion.

The amendment explicitly allows the state to ban abortion after viability , or around 23 weeks, when the fetus can survive outside the uterus, unless the pregnant woman’s doctor finds the procedure “is necessary to protect the pregnant patient’s life or health.”

But that language does not appear on the ballot. Instead, voters see a summary from the secretary of state, Frank LaRose, a Republican who opposes abortion and pushed the August ballot measure to try to thwart the abortion rights amendment. That summary turns the provision on viability on its head, saying the amendment “would always allow an unborn child to be aborted at any stage of pregnancy, regardless of viability.”

Other Republicans have helped spread misinformation about the amendment. The state attorney general, who opposes abortion, issued a 13-page analysis that said, among other claims, that the amendment could invalidate a law requiring parental consent for minors seeking abortion. ( Constitutional scholars have said these claims are untrue . And the amendment would allow some restrictions on abortion.)

The ballot measure Republicans put forward in August trying to make this one harder to pass was also called Issue 1. Across the state, some lawns still have signs up from abortion rights groups urging “No on Issue 1.”

Abortion rights groups have reminded voters of the consequences of Ohio’s six-week abortion ban that was in effect for 82 days last year — and could go into effect again any day, pending a ruling from the state’s Supreme Court. They repeatedly mention the 10-year-old rape victim who traveled to Indiana for an abortion after doctors in Ohio refused to provide one because of the ban.

In a television ad, a couple tells of their anguish when doctors told them at 18 weeks that a long-desired pregnancy would not survive, but that they could not get an abortion in Ohio, forcing them, too, to leave the state for care: “What happened to us could happen to anyone.”

The “yes” side has also appealed to Ohioans’ innate conservatism about government overreach, going beyond traditional messages casting abortion as critical to women’s rights. John Legend, the singer-songwriter and Ohio native whose wife, Chrissy Teigen, has spoken publicly about an abortion that saved her life, urged in a video message, “Issue 1 will get politicians out of personal decisions about abortion.”

The “no” side makes little mention of the six-week ban, or abortion. Yard signs and billboards instead argue that a “no” vote protects parents’ rights. Protect Women Ohio has spread messages on social media and in campaign literature claiming that because the amendment gives “individuals” rather than “adults” the right to make their own reproductive decisions, it could lead to children getting gender transition surgery without parental permission — which constitutional scholars have also said is untrue.

The anti-abortion side is trying to reach beyond the conservative base, and it will have to in order to win. In polls in July and October, 58 percent of Ohio residents said they would vote in favor of the amendment to secure abortion rights, and that included a majority of independents.

Kristi Hamrick, the vice president of media and policy for Students for Life, which opposes abortion and has been “dorm knocking” on college campuses in Ohio, said the anti-abortion side had relied too much on “vague talking points” to try to win earlier ballot measures. “It wasn’t direct in what was at stake and how people would be hurt,” she said. “What is at stake is whether or not there can be limits on abortion, whether we can have unfettered abortion.”

In Ohio, the anti-abortion side has leaned into arguments that the amendment would encourage “abortion up until the moment of birth.” An ad aired during the Ohio State-Notre Dame football game featured Donald Trump warning, “In the ninth month, you can take the baby and rip the baby out of the womb of the mother.”

Data shows late-term abortions are rare and usually performed in cases where doctors say the fetus will not survive. In Ohio, there were roughly 100 abortions after 21 weeks of pregnancy in 2020.

National groups have poured in money, making this an unusually expensive off-year race. Ohioans United for Reproductive Rights, the coalition of abortion rights groups supporting the amendment, has spent $26 million since Labor Day, nearly three times as much as Protect Women Ohio, and most of that money has come from outside the state.

At the market hall, the group of pediatricians leading the canvass for the “yes” side landed mostly on people who had heard about the amendment and supported it.

One voter, Ashley Gowens, introduced herself to one of the doctors as “Stephanie’s mom,” thanking him for “standing up for my daughter’s rights.” Ms. Gowens worried that abortion rights supporters would be misled by the language on the ballot, or not realize they had to vote again — and differently — after the August election called by Republicans. “I know that it was done purposefully,” she said. “The only way they could knock this down was to confuse people.”

David Pepper, a former state Democratic Party chair, said he too feared the August election had sapped some energy, and that the anti-abortion messages against extremism will appeal to Ohioans’ reluctance to change their Constitution.

“You kind of have to run the table on your arguments, and they all have to be pretty persuasive for people to vote yes,” he said. “All you have to do to convince someone to vote “no” is give them one reason.”

Kate Zernike is a national correspondent. She was a member of a team that shared a 2002 Pulitzer Prize for a series of stories about Al Qaeda and the Sept. 11 attacks. She is the author of “The Exceptions: Nancy Hopkins, MIT, and the Fight for Women in Science.” More about Kate Zernike

Lisa Lerer is a national political correspondent, covering campaigns, elections and political power. More about Lisa Lerer