Convenient Fires and Floods and Impossible Archival Imaginaries
Bibliographic record
Abstract
This article concerns one notable feature of narratives around child welfare records: the prevalence of stories of records destroyed in natural disasters. These stories have the power to rouse strong emotions for people who grew up in institutional “care.” Care Leavers, many of whom have a justifiable lack of trust in institutions and authority as a result of their childhood experiences, are skeptical about the supposed loss of their records in fires and floods. They remain suspicious that the records do exist but are being withheld to protect the reputations of the institutions. This article considers Gilliland and Caswell’s notion of “archival imaginaries” in the context of missing, lost, or inaccessible child welfare records in Australia. The authors argue for an approach to describing these records that is not only person centred but also trauma-informed. The article presents two case studies that demonstrate the potential of applying this approach when describing records supposedly destroyed by fires and floods. Descriptions need to document the full story of the records, whether they materially exist or not, in a way that validates and acknowledges Care Leavers’ strong feelings about records and demonstrates archival organizations’ commitment to remediating the damage and hurt caused by past practices.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.048 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| 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".