Bibliographic record
Abstract
describe the future of the journal.John is the father of the journal.More than five years ago, John realised that the field of numerical and mathematical cognition had matured to such an extent that it might benefit from a place where specialised research could be presented to a specialised audience.John didn't only passively realise this.He also took action.Having explored possibilities, discussed pros and cons with colleagues, he founded the journal in 2015 through the PsychOpen GOLD platform (www.psychopen.eu), a publishing service and infrastructure provided by Leibniz Institute for Psychology Information (ZPID), Trier, Germany.This wasn't without risk.An earlier attempt to create a journal dedicated to the field, Mathematical Cognition, launched in 1995, had turned out to be hard to sustain after a successful start.Apparently back in the 90's, the time wasn't ripe yet.John had a good intuition when sensing that this time was the right time.But, evidently, intuition is not enough.What has been the crucial pillar for the success is that he has invested a lot of time, dedication and energy in getting the journal off the ground.Content-wise, John had felt that it was necessary to cover the full width of the domain of numerical cognition.From the beginning onwards, John has adhered to a broad scope for the journal.A broad scope in topics (from basic number representations to education), a broad scope in disciplines (from cognitive psychology to intervention research) and a broad scope in format (not only research reports but also, book reviews, commentaries and theoretical contributions).But there is one thing where John stuck to a narrow scope and that is quality.John has always guarded quality, which for a journal is extremely important: The reader must be sure that the paper she decides to read is of uncompromised quality.It is not easy to accomplish this.John did this with the
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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".