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Hype or Real Threat: The Extent of Predatory Journals in Student Bibliographies

2019· article· en· W2927052045 on OpenAlexaffvenue
Rainer Schira, Chris Hurst

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2019
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsBrandon University
Fundersnot available
KeywordsPublishingLibrary scienceComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Predatory publishing has risen with the development of open access publishing. This study examines how many potential predatory journals were used by Brandon University students by analyzing their bibliographies. In total, 245 bibliographies including 2,359 citations were analyzed. Of the 1,485 citations to journals in these citations, five were found to cite journals on Beall’s List of Predatory Journals and Publishers. The probable sources of these journals in the students’ bibliographies were examined.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometrics
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometricsScholarly communication
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.019
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.180
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0210.023
Science and technology studies0.0020.005
Scholarly communication0.0060.007
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.495
GPT teacher head0.564
Teacher spread0.068 · 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

Labeled directly by 2 models reading the full record.

MetaresearchBibliometricsScholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
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

Citations12
Published2019
Admission routes2
Has abstractyes

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