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Record W3080187847 · doi:10.1177/2056305120943273

Studying Platforms and Cultural Production: Methods, Institutions, and Practices

2020· article· en· W3080187847 on OpenAlex
David B. Nieborg, Brooke Duffy, Thomas Poell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Media + Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTemporalitiesSoftware walkthroughParticipant observationProduction (economics)SociologyCultural analysisData collectionData scienceKnowledge managementComputer scienceSocial sciencePolitical scienceSoftware

Abstract

fetched live from OpenAlex

This introduction to the second special collection of articles on the platformization of the cultural industries foregrounds research methods and practices. Drawing from the 12 articles included in this collection, as well as the 14 articles published in the first collection, we identify commonalities in approaches, consistencies in traditions, and uniform modes of analysis. We argue that approaches that have been deployed in media industry studies for decades—semi-structured interviews, discourse analysis, content analysis, and participant observation—remain productive. At the same time, transformations in the temporalities and curation of cultural production require updated modes of investigation and analysis. As such, we spotlight contributors’ novel methods and innovative theoretical approaches, such as the walkthrough method and multi-sided market theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.321
GPT teacher head0.438
Teacher spread0.117 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it