Canadian psychology is basic, applied, and impacts the world: An editorial about the next four years.
Bibliographic record
Abstract
Like any new editor, I have set some goals for Canadian Psychology/Psychologie canadienne (CP/PC). My first goal is to increase the international profile of our journal while maintaining its Canadian character. My second goal is to increase the profile of basic science within the journal without diminishing the focus on areas. The first step in achieving these goals involved making some changes to the structure of the editorial board. You will notice that on the masthead, in addition to the many distinguished Canadian researchers who make up our board, we have added a complement of international board members who are authorities in their discipline and who have already started to provide valuable input on important scientific matters. Their presence will increase the visibility of the journal around the world. Our editorial board may be more diverse than ever, as it includes distinguished academics representing both and basic areas. Their expertise span from biopsychology to psychology, from cognitive psychology to professional ethics and standards, and from perception and psychophysics to industrial psychology. Many of my colleagues have told me that they always considered CP/PC to be a clinical or applied journal. This is not so. We welcome important papers from any area of psychology, including, but not limited to, biopsychology, clinical, cognitive, educational, history of psychology, quantitative, social, and so on. We aim to publish papers that would be of interest to large cross sections of psychologists spanning across more than one area of specialty. Topics such as neuronal regeneration, ethics in psychology, and history of our field all fit this category. In an effort to demonstrate the broad mandate of Canadian Psychology/Psychologie canadienne, I have decided to publish a special section on D. 0. Hebb (this issue). I distinctly remember when I first came across Hebb's name. I was a high school senior in Thessaloniki, Greece; I was sitting on a park bench when I read in a newspaper that a psychologist from McGill University in Montreal had changed the way we think about the brain and behaviour. While attending McGill University, I was fortunate to study Introductory Psychology under Ronald Melzack, who offered a course that was largely focused on Hebb's ideas. Hebb, a basic scientist, had a great impact around the world and we are all proud that he has been one of the great minds of Canadian psychology. In this issue, world-class commentators re-evaluate Hebb's neural network learning rule in relation to all relevant new knowledge that has accumulated over the last several decades. We have also included a review of Hebb's classic text, The Organization of Behavior. Although the book was originally published in 1949, it was re-released in 2002 by Lawrence Erlbaum Associates. Richard Tees reviews the book from a contemporary perspective. Coincidentally, one of the papers that was accepted by the previous editor, Vic Catano, was related to Hebb's neural network learning rule. This paper by Lord, Hanges, and Godfrey complements the section on Hebb and, as such, I decided to include it in this issue. …
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".