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Record W2801067405 · doi:10.1002/fee.2084

Researcher engagement in policy deemed societally beneficial yet unrewarded

2019· review· en· W2801067405 on OpenAlexafffund
Gerald G. Singh, Vinicius F. Farjalla, Bing Chen, Andrew E. Pelling, Elvan Ceyhan, M. Dominik, Eva Alisic, Jeremy T. Kerr, Noelle E. Selin, Ghada Bassioni, Elena M. Bennett, Andrew H. Kemp, Kai M. A. Chan

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2019
Typereview
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of OttawaMcGill UniversityMemorial University of NewfoundlandUniversity of British ColumbiaFisheries and Oceans Canada
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNational Health and Medical Research CouncilSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsPublic relationsBusinessEnvironmental resource managementEnvironmental planningPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

Maintaining the continued flow of benefits from science, as well as societal support for science, requires sustained engagement between the research community and the general public. On the basis of data from an international survey of 1092 participants (634 established researchers and 458 students) in 55 countries and 315 research institutions, we found that institutional recognition of engagement activities is perceived to be undervalued relative to the societal benefit of those activities. Many researchers report that their institutions do not reward engagement activities despite institutions' mission statements promoting such engagement. Furthermore, institutions that actually measure engagement activities do so only to a limited extent. Most researchers are strongly motivated to engage with the public for selfless reasons, which suggests that incentives focused on monetary benefits or career progress may not align with researchers' values. If institutions encourage researchers' engagement activities in a more appropriate way - by moving beyond incentives - they might better achieve their institutional missions and bolster the crucial contributions of researchers to society.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.351
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations25
Published2019
Admission routes2
Has abstractyes

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