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Record W2101597439 · doi:10.1177/1350508411403531

Free-Riding on Power Laws: questioning the validity of the Impact Factor as a measure of research quality in organization studies

2011· article· en· W2101597439 on OpenAlex

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

VenueOrganization · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImpact factorCredibilityPublicationObjectivity (philosophy)Quality (philosophy)Proxy (statistics)Journal rankingPublic relationsComputer sciencePolitical scienceLawCitationEpistemology

Abstract

fetched live from OpenAlex

The simplicity and apparent objectivity of the Institute for Scientific Information’s Impact Factor has resulted in its widespread use to assess the quality of organization studies journals and by extension the impact of the articles they publish and the achievements of their authors. After describing how such uses of the Impact Factor can distort both researcher and editorial behavior to the detriment of the field, I show how extreme variability in article citedness permits the vast majority of articles— and journals themselves—to free-ride on a small number of highly-cited articles. I conclude that the Impact Factor has little credibility as a proxy for the quality of either organization studies journals or the articles they publish, resulting in attributions of journal or article quality that are incorrect as much or more than half the time. The clear implication is that we need to cease our reliance on such a non-scientific, quantitative characterization to evaluate the quality of our work.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.188
GPT teacher head0.364
Teacher spread0.176 · 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