CHILD WELFARE AND BLACK SURVIVORS: RESEARCH DESIGN, METHODS, FINDINGS, DISCUSSIONS AND CONCLUSION: from a Practice-based Research Paper (PRP) (THESIS) SUBMITTED TO YORK UNIVERSITY - CANADA FOR A MASTERS DEGREE (2019)
Bibliographic record
Abstract
Abstract: The researchers dissected and opened up some of the methodological conundrums that were part of the main research work. The researchers outline the methods used in this Practice-based Research Paper (PRP), linking them together and presented the demographics of the participants. Using the interview technique and the theoretical framework of narrative inquiry and phenomenology, the author analyzed data pertaining to the inadequacies of the child welfare system. These are some of the themes that emerged from the data: cultural competency, systematic barriers, poverty, affordable daycare, housing, employment, lack of counseling or therapy, burnout of workers, and large caseloads. These themes point to the complexity of being a child protection worker or Black Crown ward. Based on participants’ data, this research confirms that there is a gap in the reunification process of child welfare and families. As confirmed by participants, there still needs to be more work done within systems of child welfare, education, immigration, and other social services.
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 | 0.126 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".