Looking to the Future: What Is to Come for Comparative Cognition?
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.026 | 0.046 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".