Bibliographic record
Abstract
Why does our society think it is okay to hit children? Almost everyone thinks it is wrong to abuse a child. But many parents and teachers believe it is okay to spank children, rap their knuckles, slap their faces, pull their hair and yank their arms, as long as the punishment does not result in serious injury or death, and is intended to improve a child’s behaviour. Susan M. Turner explores the historical, psychological, sociological and legal foundations of this belief from a philosophical perspective and argues why it should be abandoned. Something to Cry About presents evidence from recent studies showing that all forms of corporal punishment pose significant risks for children and that none improves behaviour in the long term. Dr. Turner also examines Section 43 of the Canadian Criminal Code — a law that protects those who punish children in their care by allowing them to hit the children as long as such punishment is “reasonable,” even though Canadian case law shows that “reasonable” has included breaking a child’s fingers. Turner presents a comprehensive argument in favour of repeal. In Something to Cry About , Turner takes a definite stand, but does so in a way that invites critical dialogue. Her work is the first to set out the debate over corporal punishment in multidisciplinary terms pertinent to Canadian society. She brings together in one place a wide variety of thought and data which can be consulted by all Canadians concerned with the welfare of children.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".