Introduction to <i>topiCS</i> Volume 13, Issue 3
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.
Bibliographic record
Abstract
With the current issue of Topics in Cognitive Science, we are proud to present award-winning research again, starting off with a scholar's lifetime achievements distinguished by the Rumelhart Prize, then introducing the Best Papers from the 18th International Conference on Cognitive Modeling. The first topic honors Michelene (“Micki”) T. H. Chi (Arizona State University), the 19th recipient of the David E. Rumelhart Prize. With a background in mathematics and a strong interest in the science of education, Chi has conducted pioneering, widely cited, and highly influential work on the active role of learners in the learning process, from self-explanations to the development of expertise. Having been active in the society for almost her entire academic life, and elected one of its inaugural fellows in 2003, she received the award also referred to as “the Nobel Prize in Cognitive Science” in 2019, for having “challenged basic assumptions about the mind” more than once and for having “defined new approaches that have shaped a generation of cognitive and learning scientists.” Following a pithy introduction by former topiCS editor Wayne Gray, we are happy to present the paper based on her Rumelhart Lecture, on “Translating a theory of active learning,” in which she attempts to close the gap between research and practice by outlining a multistep translation research framework. The second part of this issue comprises revised and expanded versions of the four best papers presented at the 18th International Conference on Cognitive Modeling, which took place last year fully virtually as was the case for so many other conferences. This batch has been curated by Topic Editors Terrence C. Stewart (National Research Council Canada) and Christopher Myers (Air Force Research Laboratory), who also introduce this selection of papers in more detail. Congratulations to all our authors for their awards––we hope you continue the outstanding work you are doing! 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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it