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Record W7093885083

Open Access Funds: Getting a Bigger Bang for Our Bucks

2016· article· W7093885083 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2016
Typearticle
Language
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingContext (archaeology)Open access publishingBest practicePrincipal (computer security)SustainabilityScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

Many libraries offer open access publishing funds to support authors in paying article processing charges (APC) levied by some OA journals. However, there are few standard practices for managing or assessing these funds. The Open Access Working Group (OAWG) of the Canadian Association of Research Libraries (CARL) was asked to investigate and articulate best practices for successful open access fund management. In spring 2015, the OAWG surveyed Canadian academic libraries with OA funds to review their criteria and collect feedback on current practices. The survey proved timely because many OA funds are under review. Shrinking budgets, ending pilots, and questions around scale and sustainability of funds provide context for some institutions revisiting or reconfiguring these funds. At the same time, Canada’s principal funding agencies have issued the new Tri‐Agency Open Access Policy on Publications (effective May 2015) which mandates open access for funded research and which is increasing the demand from researchers for financial support from their institutions to pay APCs and other OA costs. This paper addresses findings of the survey, some best practices for open access publishing fund management, and counter arguments for OA funds, as well as other strategies developed by international agencies including the Scholarly Publishing and Academic Resources Coalition (SPARC).

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.005
Open science0.0050.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.086
GPT teacher head0.364
Teacher spread0.278 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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