Branching out while remembering our roots: A special issue on numerical and mathematical cognition.
Bibliographic record
Abstract
Over the past decades, numerical and mathematical cognition has transformed from a niche research area into a thriving global field, with contributions spanning diverse populations, methodologies, and theoretical approaches. The 13 articles in this special issue highlight the breadth and depth of contemporary research, addressing topics such as the development of early numeracy skills, the interplay between mathematical and reading processes, the cognitive mechanisms supporting arithmetic and algebra, and the role of visuospatial thinking in expert mathematical reasoning. The contributions exemplify methodological innovation, from longitudinal studies and psychometric evaluations to interdisciplinary theoretical models that integrate numerical and linguistic frameworks. Together, they collectively advance theoretical, applied, and interdisciplinary perspectives. This introduction synthesizes the contributions, demonstrating how they collectively inspire future directions for research on numerical and mathematical cognition. We discuss the broader implications of the work while also contextualizing its development within its historical ties to Canadian experimental psychology and the foundational work of pioneers such as the late Jamie I. D. Campbell, in memory of whom this special issue was conceived. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".