Bibliographic record
Abstract
For our July 2020 issue (Volume 12, Issue 3), we publish three topics. The first is a one-paper reply from Rafael Núñez and colleagues entitled, "For the Sciences They are A-Changin’: A Response to Commentaries on Núñez et al.'s (2019) 'What Happened to Cognitive Science?' " The history of this topic is unusual. It began life as a Nature Human Behavior paper (Núñez et al., 2019) which “accused” the field of Cognitive Science of being multidisciplinary rather than interdisciplinary. As this paper evoked strong feelings among many cognitive scientists, we invited 10 contributions to the topic, which we published in October 2019 (Volume 11, Issue 4). The current paper is the Núñez response to these 10 papers. Note that the response was reviewed by two distinguished members of our community and I acted as the Action Editor. Also, please note that we will not be publishing replies to the replies. However, if a group is interested in publishing a full paper on this topic, we have procedures for that, and you are invited to follow those procedures to submit a proposal for a topic on this matter. The second topic in this issue was organized and edited by Carel ten Cate (Leiden Institute for Brain and Cognition, Leiden University) and his extensive team of co-editors: Judit Gervain (Integrative Neuroscience and Cognition Center, CNRS), Clara C. Levelt (Leiden Institute for Brain and Cognition), Christopher Petkov (Newcastle University Medical School), and William Zuidema (University of Amsterdam). Their fascinating and important topic is well summarized by the title of the Editors’ Review and Introduction: Learning Grammatical Structures: Developmental, Cross-Species, and Computational Approaches. Please note that all Editors’ Reviews and Introductions written for any topic are available as free downloads courtesy of our publisher. If you are a student, new to the field, or if you are a colleague, in another area of cognitive science, interested in catching up on what your colleagues in our multidisciplinary field are doing, then these papers are what you are looking for. Our third topic in this issue is our annual Best of the International Conference on Cognitive Modeling (ICCM) edited this year by Terrance C. Stewart (Associate Research Officer at the National Research Council Canada) and Christopher W. Myers (U. S. Air Force Research Laboratory). ICCM is a small group of researchers which has been holding meetings and publishing proceeding for about 30 years. As a small group, it has evolved rapidly over time with the one constant (maybe) being its focus on models of human behavior. The group’s original focus on computational models has been augmented lately by mathematical and statistical modeling. Interestingly, as Stewart points out in his introduction, this year none of their “best-ofs” are modeling the mythical average human; rather all of their best papers have something to do with modeling the performance and cognition of individual humans. The ICCM best-ofs has always been a solid and sometimes exciting collection of papers. For those of us interested in understanding “individuals,” it has just become more exciting. To our readers, keep searching and reading topiCS for our high-quality, curated collections of papers on timely topics of interest to the broad cognitive science community. topiCS encourages letters and commentaries on all topics, and proposals for new topics. Letters are typically 400–1,000 words (maximum of two published pages) and will be published without an abstract or references (possibly 1–2 but usually none). Commentaries are often solicited by Topic Editors prior to the publication of their topic. However, commentaries after publication are also considered and should range between 1,000 and 2,000 words. Most commentaries would not have an abstract and would not include many references. The Executive Editor and the Senior Editorial Board (SEB) members are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor (mail to: [email protected]) or an SEB member (SEB members are listed under the Editorial Board heading on 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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.213 | 0.190 |
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".