Bibliographic record
Abstract
This special section on numerical cognition is an outcome of the 33rd meeting of the Banff Annual Seminar in Cognitive Science (BASICS) held in May, 2014. Valerie A. Thompson, also of the University of Saskatchewan, and I were co-organisers of BASICS 2014. For the overarching theme of the conference we decided to focus on the field of numerical cognition, which, by no coincidence, happens to be my main research interest. The importance of numeracy to modern societies can hardly be overemphasised. We are immersed in numbers and basic calculations every day and rely heavily on technologies that would be impossible without sophisticated mathematical knowledge. In the world's increasingly computer-based technological economy, numeracy is at least as important to socioeconomic opportunities as literacy, although the latter has historically received far more research attention. Over the last four decades, however, there has been a major escalation of research in the behavioural and brain sciences directed to understanding numeracy skills. This research intersects numerous subfields including cognitive and neurological development, educational psychology, computational modelling, animal cognition, neuropsychology, as well as cognitive science and neuroscience, including brain imaging and brain stimulation research (Campbell, 2005; Cohen Kadosh & Dowker, 2015).When the opportunity to help organise BASICS 2014 came along, as a long-time contributor to numerical cognition research I set out to recruit top people in the field, hoping to include both researchers who helped establish numerical cognition as a core area of research and also rising stars who were already redefining the future of the field. To my absolute delight, the first five people on my list all agreed more or less instantly to attend and present at the 2014 conference. These included Daniel Ansari (Western University, Canada), Roi Cohen Kadosh (Oxford University, U.K.), Wim Fias (Ghent University, Belgium), Jo-Anne LeFevre (Carleton University, Canada), and Patrick Lemaire (Aix-Marseille Universite, France). One possible reason for the uniform enthusiasm among this distinguished group to present at BASICS 2014 might have been my status as one of the pioneers of this research area. Of course, on the other hand, the world-renowned scenic beauty of Banff National Park, fascinating and ever-present wildlife, extensive recreational opportunities, and exquisite cuisine promised might have also played a small role in our having successfully attracted this great group to the conference. But let us not forget that the historical record of BASICS presenters includes a virtual pantheon of cognition and neuroscience greats (Peter Dixon, a long-time co-organiser of BASICS from the University of Alberta, has maintained a record of the conference at http://www.psych .ualberta.ca/~pdixon/BASICS/2014/PastBASICSPage/index.html.BASICS provides each speaker (usually five presentations over 2 days) an hour and a half for their presentation and discussion period, so that a speaker has the opportunity to talk about their research in big-picture fashion. Given this, our selection of speakers was not based solely on their published contributions to the numerical cognition literature, but also on their reputations as fine speakers. By the end of the morning session on the second day of the conference (Jo-Anne LeFevre, Daniel Ansari, and Patrick Lemaire had presented) there was already a buzz around the conference that something special was in progress. By the end of the afternoon session (Wim Fias and Roi Cohen Kadosh presented) there was a strong vibe that the group of speakers had collectively provided a fascinating, exciting, even inspiring picture of the history and future of numerical cognition research. Immediately following Valerie Thompson's closing remarks after the last speaker, CJEP Editor-in-Chief Penny Pexman, who was in attendance, suggested to me that the set of BASICS 2014 presentations would potentially make an excellent special section for the journal. …
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.160 | 0.080 |
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".