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Record W3195597029 · doi:10.1007/s11192-021-04118-3

A credit-like rating system to determine the legitimacy of scientific journals and publishers

2021· article· en· W3195597029 on OpenAlexfundno aff
Jaime A. Teixeira da Silva, Daniel J. Dunleavy, Mina Moradzadeh, Joshua Eykens

Bibliographic record

VenueScientometrics · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
FundersVlaamse regeringThompson Rivers University
KeywordsEconomic JusticePublishingCredit ratingQuality (philosophy)Actuarial scienceBusinessPolitical scienceLawEpistemology

Abstract

fetched live from OpenAlex

The predatory nature of a journal is in constant debate because it depends on multiple factors, which keep evolving. The classification of a journal as being predatory, or not, is no longer exclusively associated with its open access status, by inclusion or exclusion on perceived reputable academic indexes and/or on whitelists or blacklists. Inclusion in the latter may itself be determined by a host of criteria, may be riddled with type I errors (e.g., erroneous inclusion of a truly predatory journal in a whitelist) and/or type II errors (e.g., erroneous exclusion of a truly valid scholarly journal in a whitelist). While extreme cases of predatory publishing behavior may be clear cut, with true predatory journals displaying ample predatory properties, journals in non-binary grey zones of predatory criteria are difficult to classify. They may have some legitimate properties, but also some illegitimate ones. In such cases, it might be too extreme to refer to such entities as "predatory". Simply referring to them as "potentially predatory" or "borderline predatory" also does little justice to discern a predatory entity from an unscholarly, low-quality, unprofessional, or exploitative one. Faced with the limitations caused by this gradient of predatory dimensionality, this paper introduces a novel credit-like rating system, based in part on well-known financial credit ratings companies used to assess investment risk and creditworthiness, to assess journal or publisher quality. Cognizant of the weaknesses and criticisms of these rating systems, we suggest their use as a new way to view the scholarly nature of a journal or publisher. When used as a tool to supplement, replace, or reinforce current sets of criteria used for whitelists and blacklists, this system may provide a fresh perspective to gain a better understanding of predatory publishing behavior. Our tool does not propose to offer a definitive solution to this problem.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.070
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.058
GPT teacher head0.281
Teacher spread0.223 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainEvaluation
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

Citations29
Published2021
Admission routes1
Has abstractyes

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