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Record W2019640123 · doi:10.1080/0004867010060502

The Impact Factor: Time for Change

2001· article· en· W2019640123 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

VenueAustralian & New Zealand Journal of Psychiatry · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsImpact factorPublishingPromotion (chess)Subject (documents)PsychologyQuality (philosophy)Vulnerability (computing)CriticismApplied psychologyPublic relationsComputer sciencePolitical scienceLawEpistemologyLibrary science

Abstract

fetched live from OpenAlex

OBJECTIVE: The Impact Factor (IF) has received virtually no attention in the psychiat ric literature, despite its long-term use, expanding influence and evidence of misapplication. We examine the IF's validity as a measure of a paper's scientific worth, and consider alternative ways to conduct such an appraisal. METHOD: We explored medical databases and websites, and conferred with acknowledged experts on the subject. RESULTS: Irremediable problems, both conceptual and technical, make the IF a flawed measure. The notion that citations vouch for the quality of an article is questionable. Moreover, the IF's vulnerability to misuse in domains such as academic promotion and research grant assessment is a serious development. CONCLUSION: The IF (and all measures derived from it) should be abandoned. A "return to basics" in evaluating published work is overdue. As seductive as a simple formula is to assess quality, shortcuts are unavailable and unlikely to be useful. Publishing a short-list of papers annually, judged as objectively as possible by peers to merit special attention, may be a more meaningful option. Conceivably, every psychiatric journal could participate in this cyclical exercise, leading to a "grand short-list". This could be made readily available to all professionals, both researchers and clinicians, by being posted on a suitable website. Since peer review has a long-standing role in scientific publishing, our proposal is essentially an extension of that process.

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)0.025
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.605
GPT teacher head0.524
Teacher spread0.081 · 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