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Record W4252626067 · doi:10.1111/tops.12521

Introduction to Volume 12, Issue 4 of <i>topiCS</i>

2020· article· en· W4252626067 on OpenAlexaboutno aff
Wayne D. Gray

Bibliographic record

VenueTopics in Cognitive Science · 2020
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsPublicationEditorial boardArgument (complex analysis)PublishingLibrary scienceExecutive summaryGray (unit)Media studiesComputer scienceSociologyPolitical scienceLawMedicine

Abstract

fetched live from OpenAlex

For our October 2020 issue (Volume 12, Issue 4), we publish two topics. However, before I describe those, I want to note that this is the last issue of topiCS for me as the Founding and Executive Editor of this journal. This introduction is no place to recount all the details of the origin of the journal, but let me begin by saying that it got its final boost during the Governing Board meeting at the 2006 Conference (Vancouver, Canada). At that time I was still a member of the Executive Committee of the Cognitive Science Society, and I remember leading the arguments both for and against starting a new journal. The main argument against a new journal was that we already had a fine first journal: namely, the Cognitive Science journal. At that time we could not envision a second journal that could stand alone by representing a subset of the field. (Interesting, because now I certainly can imagine that!) However, also at that time I was finishing up work on an edited volume I had organized (Gray, 2007) with me as the sole editor, with many fine cognitive scientists as contributors. I remember thinking how unfair it was to them that, unlike for a journal article, the main editor's name would be the only name shown on the cover, as well as the name in the largest font on the publisher's website and paperbase descriptions being distributed by the vendor at the Vancouver Conference. During the arguments, everything clicked into place and I proposed that we create a journal in which guest editors would recruit authors and organize papers much the way as editors of edited books did. The rest, as they say, is history. And I am very pleased to turn the journal over, after 12 years of being the Executive Editor (which followed about 2 years of negotiations with our publisher, and then recruiting Topic Editors and authors for our first two issues). topiCS's second Executive Editor will be Andrea Bender of University of Bergen, Institutt for samfunnspsykologi, Norway. Andrea has extensive experience with topiCS having organized several topics with other researchers as members of the Editor team. Likewise, she has served for many years on my Senior Editor Board which helps recruit, review, and vet all proposals sent to topiCS. Yes, dear reader, you are in good hands and, indeed, I cannot think of better ones to continue and deepen the contribution of this journal to our field. Okay … enough history, back to work. This issue has two excellent topics, both of which are exciting examples of how cognitive scientists can use topiCS when presenting their ideas to the wider community. The first topic was proposed, organized, and lovingly edited by Henry Prakken, Floris Bex and Anne Ruth Mackor and is titled “Models of Rational Proof in Criminal Law.” In addition to an excellent Editors' Introduction and six fine papers, each of which is based on the famous Simonshaven Case, the Editors have recruited interesting commentaries from four members of our community. The second topic has been organized and shepherded through to publication by Matteo Colombo and Markus Knauff. This is a brilliantly executed look at “Levels of Explanation in Cognitive Science: From Molecules to Culture.” I can personally endorse this topic, outside my role as Executive Editor, as once most of the papers were in Early View this past spring, I used a large subset of them in my graduate research seminar. The “levels of explanation” approach worked well and helped the students see how our field is joined together despite the sometimes very disparate nature of our theories and data. Indeed, this topic may well be the best response we have to the recent battles over whether Cognitive Science is multidisciplinary and not interdisciplinary (Gray, 2019; Núñez et al., 2019, 2020). 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. 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 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 (mailto: [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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.355
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0120.004
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.3550.322

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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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