Bibliographic record
Abstract
This paper evolved from the outcome of a feedback meeting held between the principle researchers of Cycle II of the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS), the Public Health Agency of Canada (PHAC) and a number of representatives of the First Nations Child and Family Service Agencies (FNCFS Agencies) which participated in Cycle II of the CIS (CIS-2003) and numerous Research Assistants tasked with collecting information from the FNCFS Agencies. The authors present a profile of the historical and contemporary experience of Aboriginal children and families who come into contact with the child welfare system and include a discussion on some of the findings from two analyses that have been conducted on the data from the 1998 Canadian Incident Study of Reported Child Abuse and Neglect (CIS-1998). An overview of the challenges as well as the positive aspects of the study from the perspectives of the FNCFS Agencies and the Research Assistances is included along with an examination as to why research may not figure prominently among the service priorities of FNCFS Agencies. The strengths of challenges of participating in CIS-2003 provide rich insight into the perspectives of the Research Assistants and FNCFS Agencies who participated in this national study. The paper concludes with recommendations by the FNCFS Agencies and the Research Assistants on how to improve the data collection process with FNCFS Agencies for future Cycles of the Canadian Incident Study of Reported Child Abuse and Neglect.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".