Bibliographic record
Abstract
The first of this year's Topics in Cognitive Science issues is entirely devoted to excellent and award-winning research. We kick off with the newest contribution to the (ongoing) topic aimed at introducing the Fellows of the Cognitive Science Society (outlined in Bender, 2022), so far featuring articles by Cleotilde “Coty” Gonzalez (Gonzalez, 2022), Steven Sloman (Sloman, 2022), Jenny R. Saffran (Ruba, Pollak, & Saffran, 2022), and Michael J. Spivey (Spivey, 2023). This exclusive group is now joined by Barbara Malt from Lehigh University (US), who was elected Fellow of the Cognitive Science Society in 2022. In her paper “Representing the world in language and thought,” Malt (2024) provides an overview of her lifelong research program. Leveraging a remarkable variety of methodological paradigms, settings, and samples, this research has investigated the relationship between the content of thoughts and the words expressing them, and explored to what extent the latter open a window into the former. As Barbara pointed out in a personal note to me, the invitation to write a Fellows paper came at the right time, just as she was preparing for retirement. My wish is for her to fully embrace this new freedom, allowing herself to devote more time to the activities she holds dear, while I also hope that, even in retirement, she will continue to be a source of inspiration for the field of cognitive science. Incidentally, the final topic in this issue, too, presents Best Papers, in this case from the 20th International Conference on Cognitive Modeling in 2022. For this topic, Terrence C. Stewart (National Research Council Canada) has assembled the revised and expanded versions of the four top-ranked papers across both the virtual and in-person events of that year's ICCM, which was the first fully hybrid version of the conference. As Stewart points out in his introduction, these papers leverage cognitive modeling to test novel computational theories, account for the generation of high-level phenomena from low-level components, and develop novel explanations of human performance in complex tasks. topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstract and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.307 | 0.218 |
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".