Risk, Morality, and Child Protection: Risk Calculation as Guides to Practice
Bibliographic record
Abstract
Initially found in population studies designed to discover a link between child abuse and population categories, risk has been institutionalized in British Columbia through the use of a risk assessment tool presumed to measure danger to particular children. Recruitment of the risk speech genre reflects a need for government child protection workers to clearly articulate which children are in need of protection from “risks as they really are” while avoiding the accusation of “intervening too much.” Moreover, risk assessment tools are both audit systems and auditable systems connected through an audit chain. Auditors who represent potential blaming procedures track errors in risk valuation to specific decisions. This entrenches a separation between what we want to know about risk from what we want to do about risk. The apparently objective use of numeric values within risk assessments obscures the continuing role of subjective moral values in the practice of child protection.
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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 | 0.052 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".