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Referee report. For: Top health research funders’ guidance on selecting journals for funded research [version 1; peer review: 1 approved, 1 approved with reservations]

2021· article· en· W4416779595 on OpenAlexfundno aff
Nicholas DeVito

Bibliographic record

VenueFaculty of 1000 Research Ltd · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
FundersMichael Smith Health Research BC
KeywordsHealth careMEDLINEPublic healthMedical researchQuality (philosophy)Peer review

Abstract

fetched live from OpenAlex

Background: Funded health research is being published in journals that many regard as “predatory”, deceptive, and non-credible. We do not currently know whether funders provide guidance on how to select a journal in which to publish funded health research. Methods: We identified the largest 46 philanthropic, public, development assistance, public-private partnership, and multilateral funders of health research by expenditure, globally as well as four public funders from lower-middle income countries, from the list at https://healthresearchfunders.org . One of us identified guidance on disseminating funded research from each funders’ website (August/September 2017), then extracted information about selecting journals, which was verified by another assessor. Discrepancies were resolved by discussion. Results were summarized descriptively. This research used publicly available information; we did not seek verification with funding bodies. Results: The majority (44/50) of sampled funders indicated funding health research. 38 (of 44, 86%) had publicly available information about disseminating funded research, typically called “policies” (29, 76%). Of these 38, 36 (95%) mentioned journal publication for dissemination of which 13 (36.11%) offer variable guidance on selecting a journal, all of which relate to the funder’s open access mandate. Six funders (17%) outlined publisher requirements or features by which to select a journal. One funder linked to a document providing features of journals to look for (e.g. listed in the Directory of Open Access Journals) and to be wary of (e.g., no journal scope statement, uses direct and unsolicited marketing). Conclusions: Few funders provided guidance on how to select a journal in which to publish funded research. Funders have a duty to ensure that the research they fund is discoverable by others. This research is a benchmark for funder guidance on journal selection prior to the January 2021 implementation of Plan S (a global, funder-led initiative to ensure immediate, open access to funded, published research).

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)0.025
metaresearch head score (Gemma)0.316
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.975
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.316
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.005
Science and technology studies0.0070.002
Scholarly communication0.0090.006
Open science0.0050.005
Research integrity0.0210.013
Insufficient payload (model declined to judge)0.4450.318

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 head0.581
GPT teacher head0.590
Teacher spread0.009 · 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 designNot applicable
DomainIncentives
GenreCommentary

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".

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Citations0
Published2021
Admission routes1
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