MétaCan
Menu
Back to cohort
Record W4308656581 · doi:10.1080/08989621.2022.2145470

“Researchers’ perceptions and awareness of predatory publishing: A survey”

2022· article· en· W4308656581 on OpenAlexaff
Sweety Angelirie Kharumnuid, Poonam Singh Deo

Bibliographic record

VenueAccountability in Research · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsScience North
Fundersnot available
KeywordsPublishingReputationCommissionTransparency (behavior)Public relationsResearch ethicsLibrary sciencePolitical scienceSociologyPsychologyLaw

Abstract

fetched live from OpenAlex

The term “Predatory” alludes to the assumption that these organizations prey on academics for financial gain by charging article processing charges (APC) while failing to meet scholarly publishing standards.Predatory publishing is a growing threat to the academic society. Considering this,the University Grants Commission (UGC),India’s statutory body for higher education,has responded by launching the University Grants Commission–Consortium for Academic Research and Ethics (UGC-CARE) list,which attempts to promote research quality,integration,and publication ethics.An online survey was undertaken to determine the perception and awareness of North Eastern Hill University’s researchers concerning predatory journals.A total of 160 respondents were recorded.The survey reveals that while the majority of participants (58.75%) were aware of predatory publications, a significant portion (41.25%) were not.It was found that a journal’s listing in UGC-CARE list is the most crucial factor in submitting an original manuscript for publication.Researchers,aware of the negative consequences of publishing in piracy-related publications,prefer not to submit their scientific work to such publishers as it risk tarnishing their reputation.As a result,research findings emphasize the necessity for awareness initiatives to educate researchers about predatory publications early in their academic careers.Research initiatives like the UGC-CARE list should be encouraged to minimize predatory publishing; promote quality and transparency in research.Abbreviation: NEHU- North Eastern Hill University, UGC- University Grants Commission, APC- Article Processing Charge, UGC-CARE- University Grants Commission - Consortium for Academic Research and Ethics, DOAJ- Directory of Open Access Journals, DOI - Digital Object Identifiers, API- Academic Performance Indicator

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)0.012
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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 score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.639
GPT teacher head0.582
Teacher spread0.057 · 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 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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAccountability in ResearchSame topicAcademic Publishing and Open AccessFrench-language works237,207