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Record W4300786865 · doi:10.46692/9781447313656.004

Apologies, memorials and other acknowledgements

2015· other· en· W4300786865 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceArchaeologyGeography

Abstract

fetched live from OpenAlex

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, 2011) 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. 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. All these initiatives attempt to change the way in which the past is understood and remembered. They contribute to shifts in understandings of care and present-day relationships between adult care-leavers and the wider community. In this chapter, we first consider the forms of acknowledgement, their purpose and importance, and key characteristics. We then review what has occurred in each of the countries under investigation. Specific examples of the various forms of acknowledgements are analysed to see to what extent they include features that have been identified as critical to their performance as acknowledgements. Particular attention is paid to Australia, Canada and Ireland, where national apologies and remembrance initiatives have been put in place.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0070.010
Open science0.0010.009
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0420.016

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.

Opus teacher head0.182
GPT teacher head0.511
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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