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Record W4412721390 · doi:10.1139/facets-2024-0113

Designing a collective prototype of future (sub)tropical science

2025· article· en· W4412721390 on OpenAlexaffvenue
Gracielle Higino, Krishna Anujan, Mickey Boakye, M. Eugenia Degano, Norma-Rocio Forero-Muñoz, Tanya Strydom

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Ecologists coming from certain countries are currently disfavoured on the peer review process, the recognition of their research products and academic labour, and the lack of leadership opportunities in global settings. Additionally, we often witness deliberate obfuscation of (sub)tropical ecologists on matters pertaining to our own communities, and we believe that putting these researchers at the centre of the discussion on the practice of tropical science is urgent. Breaking barriers for early career researchers with lived, living, and working experience in the tropical and subtropical regions to lead and contribute meaningfully to their own communities will be decisive to revamp academic culture, as well as to bring the necessary change to public policies affecting local ecosystems. In this perspective, we acknowledge the multiscale efforts needed to shape the future of ecology as a profession, delineating the effects of social spheres outside academia, academic and adjacent institutions, and the scientific community on the opportunities available for (sub)tropical researchers.

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)0.027
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0120.019
Scholarly communication0.0120.011
Open science0.0030.013
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0220.003

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 head0.252
GPT teacher head0.455
Teacher spread0.204 · 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 designQualitative
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
Published2025
Admission routes2
Has abstractyes

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