Bibliographic record
Abstract
This book relies on witnesses' depositions and related evidence held in New Zealand inquest files.On two occasions Coronial Services transferred blocks of ever more recent files to Archives New Zealand.Each time that an accession occurred, I advanced the cutoff date for this study, until it was feasible to conclude in 2000.The prospect of covering a century was an opportunity too important to ignore; however, that ambition strained the budget by adding roughly four thousand files to my initial estimate.The ten years of travel needed to assemble notes and data sets required assistance from three sources.Initial core funding came from the Social Sciences and Humanities Research Council of Canada.I am grateful for this essential assistance.McMaster University, through the offices of the Provost and the Vice-President of Research, contributed too.McMaster supported a research leave for six months in 2010 when I computed data and wrote several chapters.Vital support came from Adam Weaver, who hosted my stays in Wellington during thirty months spread from June 2003 to June 2013.Adam's intellectual and moral support was also significant; his reactions to my conjectures and accounts of unhappy narratives were measured and serious.Affected both by the post-war experiences of an uncle, Albert Tamorria, who fought in the Solomon Islands, and by the Vietnam War's impact on her childhood Maryland community, Joan Weaver kept the trauma of veterans prominent in our discussions over this project.This book evolved as circumstances fell into place.Adam's appointment to Victoria University was a fortuitous circumstance.Other helpful developments followed.An early good turn of fortune
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.370 | 0.259 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".