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Record W3023134157 · doi:10.3138/jsp.51.3.01

Do Tenure and Promotion Policies Discourage Publications in Predatory Journals?

2020· article· en· W3023134157 on OpenAlexvenueaboutno aff
Fiona A.E. McQuarrie, Alex Z. Kondra, Kai Lamertz

Bibliographic record

VenueJournal of Scholarly Publishing · 2020
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingPromotion (chess)Public relationsInstitutionProductivityQuality (philosophy)Political scienceAction (physics)Academic institutionBusinessManagementEconomicsLawEconomic growth

Abstract

fetched live from OpenAlex

Predatory journals are a concern in academia because they lack meaningful peer review and engage in questionable business practices. Nevertheless, predatory journals continue to flourish, in part because of increasing expectations that academic researchers demonstrate publishing productivity in quantifiable forms. We examined tenure and promotion policies at twenty Canadian universities and did not find any language that explicitly discourages publications in predatory journals. Instead, subjective criteria such as ‘quality’ are commonly used to assess the appropriateness of publication outlets. Additionally, information on avoiding predatory journals was located only on the library’s website at nearly every institution, and the information was primarily directed at students rather than at faculty members. We argue that if predatory journals are truly a threat to the integrity of academic research and knowledge dissemination, universities must take more substantive action against them. We recommend four institutional initiatives to discourage faculty members from publishing in predatory journals.

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.

How this classification was reachedexpand

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)0.047
metaresearch head score (Gemma)0.291
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Bibliometrics, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.291
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0430.095
Science and technology studies0.0000.000
Scholarly communication0.2010.134
Open science0.0030.001
Research integrity0.0000.003
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 head0.676
GPT teacher head0.554
Teacher spread0.122 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2020
Admission routes2
Has abstractyes

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