Clinical psychology in transition: Taking responsibility and broadening the scope
Bibliographic record
Abstract
For many of us, December is the time to look back to what happened during the year.Very often we end up remembering all the challenges, difficulties, worries and burdens that have accompanied us throughout the year.This is also the case this year and not without a reason: The world is full of wars, there are crises and unstable political conditions in many countries around the globe, and not to forget the climate change that is speeding toward catastrophe (Lenton et al., 2023).But… Should we really leave this Editorial's review of the year at that?We don't think so.Even if we have seen a lot of miserable things happen in 2023, there are also a lot of positive activities going on.Or in the words of Haruki Murakami: "Where there is light, there must be shadow, and where there is shadow there must be light.There is no shadow without light and no light without shadow." (Haruki Murakami, 1Q84)
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 | 0.142 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".