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Record W4394060926 · doi:10.3819/ccbr.2024.190023

Looking to the Future: What Is to Come for Comparative Cognition?

2024· article· en· W4394060926 on OpenAlex
W. David Stahlman, Marisa Hoeschele

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComparative Cognition & Behavior Reviews · 2024
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsComparative cognitionAnimal cognitionAnimal behaviorCognitionPsychologyComparative psychologyCognitive scienceCognitive psychologyNeuroscienceBiologyZoology

Abstract

fetched live from OpenAlex

In last year's volume, we devoted some pages to reminisces.It was the 30th annual Conference on Comparative Cognition (CO3), and it struck us that certain stories from bygone years would be worth sharing.As it turns out, it was also the end of an era.We now find ourselves in new environs after decades of holding the annual meeting in Florida.At the time of this writing, we can only hope that Albuquerque in 2024 will be as hospitable as Melbourne was for many years.The change in venue serves as a reminder of the ubiquity of change, and perhaps allows us to wonder what other changes might be in store for ourselves and for our science.Thus, in a special call for papers, we asked the community to consider the future of our discipline.The response was enthusiastic.We received more than two dozen brief submissions regarding the future of comparative cognition.In this year's volume of Comparative Cognition & Behavior Reviews, we have included 22 commentaries on a wide range of topics.This brief introduction to the special issue will serve to orient you to the contents of the issue.Perhaps expectedly, with so many articles, we found that authors addressed several overarching themes.These seem to us to best represent the chief matters as pertains to the future of our field.We elected to categorize manuscripts according to these themes so as to organize the issue in a sensible fashion.This process was undertaken by us, the editors, and does not reflect any explicit input of any authors-we bear all responsibility for the following roadmap.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.160
GPT teacher head0.416
Teacher spread0.256 · 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