Bibliographic record
Abstract
The study of child maltreatment is in its infancy inCanada. Basic questions about maltreatment arejust beginning to be answered: How many children in Canada suffer abuse or neglect? What kind of maltreat-ment do they suffer? To what extent are they harmed? As physicians, how do we identify and protect children at risk? The Canadian Incidence Study of Reported Child Abuse and Neglect, the first national incidence study of child maltreatment,1 found that 2.1 % of children were the subjects of child welfare investigations in 1998. Maltreat-ment was substantiated in 45 % of these cases. This is likely an underestimate of the true incidence of child maltreat-ment because it represents only the cases that were re-ported to and investigated by child welfare authorities. Trocmé and colleagues2 now report the results for phys-ical harm associated with these substantiated cases of child maltreatment. In their study, the cases of maltreatment
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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.030 | 0.019 |
| Insufficient payload (model declined to judge) | 0.124 | 0.052 |
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".