Bibliographic record
Abstract
In Canada, our history of institutional abuse has been a tragedy of enormous proportion. It is not, sadly, only an issue of the past. (Law Commission of Canada, 20001) Harmful experiences in care are not unique to Canada. In every country where inquiries have been held, there has been shocking evidence of how poorly many children have been treated. What is more, this abuse, inadequate care and separation from family has typically resulted in long-term impacts. Furthermore, while not all of the countries under review in this book have undertaken public inquiries, there is no reason to think that the findings would be that different as three key factors were likely to have been in place: the children were vulnerable, they were relatively powerless in relation to those who cared for them and wider society was unaware of their plight or unwilling to act. At the same time, we know that some children had positive experiences in care. Having established that some children had received poor treatment, this book set out to outline what had been done about the long-term harmful effects of a childhood in care. In doing so, it sought an answer to the question: how can we best support adult care-leavers? So, when I started the research for this book, I wanted to know how the five countries of Australia, Canada, Ireland, New Zealand and the UK have responded to adult care-leavers, and what is good practice. I had begun to know something about this from my previous research doing life-history interviews with people who grew up in care in Australia and researching access to personal records about time in care.
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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".