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Record W4391753308 · doi:10.1242/dev.202342

Pluripotency of a founding field: rebranding developmental biology

2024· article· en· W4391753308 on OpenAlexaff
Crystal D. Rogers, Chris T. Amemiya, Swathi Arur, Leslie S. Babonis, Michael Barresi, Madelaine Bartlett, Richard R. Behringer, Blair W. Benham-Pyle, Dominique C. Bergmann, Ben Blackman, C. Titus Brown, Jasmin Camacho, Chiswili Chabu, Ida Chow, Ondine Cleaver, Jonah Cool, Megan Y. Dennis, Alexandra J. Dickinson, Stefano Di Talia, Margaret H. Frank, C. Stewart Gillmor, Eric S. Haag, Iswar K. Hariharan, Richard M. Harland, Aman Y. Husbands, Loydie A. Jerome‐Majewska, Kristen M. Koenig, Carole LaBonne, Michael J. Layden, Christopher J. Lowe, Madhav Mani, Megan L. Martik, Katelyn H. McKown, Cecilia B. Moens, Christian Mosimann, Joyce G. Onyenedum, Robert D. Reed, Ajna S. Rivera, Daniel S. Rokhsar, Löıc A. Royer, Flora Rutaganira, Rachel Shahan, Neelima Sinha, Billie J. Swalla, Jaimie Van Norman, Daniel E. Wagner, Athula H. Wikramanayake, Sophia G. Zebell, Siobhán M. Brady

Bibliographic record

VenueDevelopment · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsMcGill University Health Centre
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of General Medical SciencesNational Institutes of HealthNational Science Foundation
KeywordsBiologyRebrandingField (mathematics)GlobalizationDevelopmental biologyDevelopment studiesGeneticsMarketingLaw

Abstract

fetched live from OpenAlex

The field of developmental biology has declined in prominence in recent decades, with off-shoots from the field becoming more fashionable and highly funded. This has created inequity in discovery and opportunity, partly due to the perception that the field is antiquated or not cutting edge. A 'think tank' of scientists from multiple developmental biology-related disciplines came together to define specific challenges in the field that may have inhibited innovation, and to provide tangible solutions to some of the issues facing developmental biology. The community suggestions include a call to the community to help 'rebrand' the field, alongside proposals for additional funding apparatuses, frameworks for interdisciplinary innovative collaborations, pedagogical access, improved science communication, increased diversity and inclusion, and equity of resources to provide maximal impact to the community.

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)0.058
metaresearch head score (Gemma)0.035
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0160.043
Scholarly communication0.0290.035
Open science0.0030.030
Research integrity0.0070.015
Insufficient payload (model declined to judge)0.0090.002

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 head0.117
GPT teacher head0.450
Teacher spread0.334 · 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 designTheoretical or conceptual
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

Citations7
Published2024
Admission routes1
Has abstractyes

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