Bibliographic record
Abstract
The apology from Mr Rudd [Australian Prime Minister] and Turnbull [Leader of the Opposition] – some of them were jumping around: ‘Oh I’m relieved’. ‘What do you mean? You’re still the same. You can’t eat an apology.’ It’s nice to receive it but you’ll still be the same tomorrow and the day after. (Ray, adult care-leaver, 20111) There are mixed feelings about apologies and other symbolic acknowledgements of harms caused by a childhood in care. Ray, a man now aged in his 80s who grew up in care from a baby for all of his childhood, came to Parliament House to hear the apology from the Prime Minister to the Forgotten Australians in 2009. Even though he knew others found great consolation in their words, he left feeling somewhat ambivalent. Some adult care-leavers have found these symbolic acknowledgements important ways to assist in reconciling the trauma of their childhood. Others, like Ray, have found them less helpful and have sought more practical forms of recognition, as discussed in the following chapters. In Australia, while there has been a range of programmes put in place to support adult care-leavers, the federal government apologies have not included financial redress, unlike in Ireland and Canada. Typically, in response to the various inquiries, formal apologies have been issued by some governments, non-government agencies and religious organisations.2 Other forms of official remembrance have also been initiated, such as memorials and museum exhibitions. To acknowledge the importance of the children’s homes to former residents, plaques have been laid at these sites to commemorate and honour the lives of the children who lived there.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.015 |
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".