Bibliographic record
Abstract
JNC continues to grow and establish itself as a central venue for reporting and discussing contemporary ideas and knowledge in the field of mathematics and numerical cognition.JNC is superbly supported by its progressive publisher, PsychOpen, who continue to innovate and grow their publishing systems and standards whilst they maintain a Platinum publishing model -free to read and free to publish -that matches many commercial outlets.This Editorial marks the start of Volume 5 of the Journal, and there are already many important and interesting articles lined up for release this year.The Journal continues to invite and consider a broad portfolio of articles for publication."Traditional" analysis of empirical research can be complemented by pre-registered reports, where a submission is made and, following careful review, an in-principle publication decision is made prior to the collection of data.As you may notice from browsing through the Journal contents, there is an increased availability of supplementary materials for empirical research, especially raw data, and a movement towards an increasingly systematic cataloguing of what supplementary content it is possible to make available.Theoretical contributions continue to be welcomed and book reviews have been commissioned.The Journal has benefited from a number of special issues too, where a focused set of papers crystalize contemporary issues and developments.JNC reviewed and renewed its Editorial Board team members beginning in 2019.The Journal is rightly proud to have assembled an exceptional group of researchers in the field, who bring a mix of experience and new ideas, current Journal experience and also fresh talent, and a skill set that reflects the broad community of research that seeks to improve our understanding of mathematical behaviour and numerical cognition.We have full confidence that they can help to identify and nurture the very best research ideas and research findings for publication in the Journal.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.107 | 0.040 |
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".