Dear Editor: In your recent editorial (“Coasting to DSM-5 – Parental
Bibliographic record
Abstract
We are what and who we define”), you commented that “Parental Alienation Syndrome (PAS) is a good example of a syndrome trying to sneak in while everyone’s attention is diverted elsewhere. ” You mentioned some of the pros and cons of the proposition that parental alienation become an official diagnosis, and then summarily stated your conclu-sion, “I simply don’t buy the analogy argument – i.e., PAS is a relational disorder akin to DSM V-codes.” As chairman of the large, international committee that has proposed that parental alienation be included in both DSM-5 and ICD-11, I am concerned that you trivialized a sig-nificant issue confronting child and adolescent psychiatry in Canada and the U.S. Parental alienation is an important mental condition that affects hundreds of thousands of chil-dren and families, and your readers need to be aware of and understand what it is about. In Canada, the major scholars regarding parental alienation are psychologists: Abe Wo-
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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".