Updating 'The Future of Coding': Qualitative Coding with Generative Large Language Models
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| Název: | Updating 'The Future of Coding': Qualitative Coding with Generative Large Language Models |
|---|---|
| Jazyk: | English |
| Autoři: | Nga Than (ORCID |
| Zdroj: | Sociological Methods & Research. 2025 54(3):849-888. |
| Dostupnost: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 40 |
| Datum vydání: | 2025 |
| Druh dokumentu: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Coding, Qualitative Research, Cues, Open Source Technology, Accuracy, Natural Language Processing, Social Science Research |
| DOI: | 10.1177/00491241251339188 |
| ISSN: | 0049-1241 1552-8294 |
| Abstrakt: | Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using natural language, potentially mimicking the in-depth, inductive, and/or iterative process of qualitative analysis. We test the ability of generative LLMs to replicate and augment traditional qualitative coding, experimenting with multiple prompt structures across four closed- and open-source generative LLMs and proposing a workflow for conducting qualitative coding with generative LLMs. We find that LLMs can perform nearly as well as prior supervised machine learning models in accurately matching hand-coding output. Moreover, using generative LLMs as a natural language interlocutor closely replicates traditional qualitative methods, indicating their potential to transform the qualitative research process, despite ongoing challenges. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Přístupové číslo: | EJ1475799 |
| Databáze: | ERIC |
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| Header | DbId: eric DbLabel: ERIC An: EJ1475799 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Updating 'The Future of Coding': Qualitative Coding with Generative Large Language Models – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nga+Than%22">Nga Than</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6845-6253">0000-0002-6845-6253</externalLink>)<br /><searchLink fieldCode="AR" term="%22Leanne+Fan%22">Leanne Fan</searchLink><br /><searchLink fieldCode="AR" term="%22Tina+Law%22">Tina Law</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7631-6763">0000-0001-7631-6763</externalLink>)<br /><searchLink fieldCode="AR" term="%22Laura+K%2E+Nelson%22">Laura K. Nelson</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8948-300X">0000-0001-8948-300X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Leslie+McCall%22">Leslie McCall</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7700-3969">0000-0002-7700-3969</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Sociological+Methods+%26+Research%22"><i>Sociological Methods & Research</i></searchLink>. 2025 54(3):849-888. – Name: Avail Label: Availability Group: Avail Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 40 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Coding%22">Coding</searchLink><br /><searchLink fieldCode="DE" term="%22Qualitative+Research%22">Qualitative Research</searchLink><br /><searchLink fieldCode="DE" term="%22Cues%22">Cues</searchLink><br /><searchLink fieldCode="DE" term="%22Open+Source+Technology%22">Open Source Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Science+Research%22">Social Science Research</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1177/00491241251339188 – Name: ISSN Label: ISSN Group: ISSN Data: 0049-1241<br />1552-8294 – Name: Abstract Label: Abstract Group: Ab Data: Over the past decade, social scientists have adapted computational methods for qualitative text analysis, with the hope that they can match the accuracy and reliability of hand coding. The emergence of GPT and open-source generative large language models (LLMs) has transformed this process by shifting from programming to engaging with models using natural language, potentially mimicking the in-depth, inductive, and/or iterative process of qualitative analysis. We test the ability of generative LLMs to replicate and augment traditional qualitative coding, experimenting with multiple prompt structures across four closed- and open-source generative LLMs and proposing a workflow for conducting qualitative coding with generative LLMs. We find that LLMs can perform nearly as well as prior supervised machine learning models in accurately matching hand-coding output. Moreover, using generative LLMs as a natural language interlocutor closely replicates traditional qualitative methods, indicating their potential to transform the qualitative research process, despite ongoing challenges. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: EJ1475799 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/00491241251339188 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 40 StartPage: 849 Subjects: – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Coding Type: general – SubjectFull: Qualitative Research Type: general – SubjectFull: Cues Type: general – SubjectFull: Open Source Technology Type: general – SubjectFull: Accuracy Type: general – SubjectFull: Natural Language Processing Type: general – SubjectFull: Social Science Research Type: general Titles: – TitleFull: Updating 'The Future of Coding': Qualitative Coding with Generative Large Language Models Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nga Than – PersonEntity: Name: NameFull: Leanne Fan – PersonEntity: Name: NameFull: Tina Law – PersonEntity: Name: NameFull: Laura K. Nelson – PersonEntity: Name: NameFull: Leslie McCall IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0049-1241 – Type: issn-electronic Value: 1552-8294 Numbering: – Type: volume Value: 54 – Type: issue Value: 3 Titles: – TitleFull: Sociological Methods & Research Type: main |
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