Bibliographic record
Abstract
Comprising a gently fictionalised biography of my ancestor, Stephen Duckhouse, and a critical reflection this thesis combines critical and creative processes from archive and field research, family stories and creative writing. It aims to engage techniques of fictionalised biography to stage an investigation into the experiences of Home Children and the First World War. The thesis develops a practice as research methodology in parallel with critical investigations. The practice as research methodology (after Nelson, 2010) that is employed means that while I am producing a work of creative writing, I am also simultaneously presenting a critical investigation into contemporary biographical practices. By employing practice as research as a methodology, I am able to engage with a creative process woven into the creative story, with biographical research in relation to Home Children sent to Canada and the First World War, to explore and exemplify the genre of gently fictionalised biography. In terms of creative practice, I have given Stephen Duckhouse a voice via an emotional and spiritual response, which justifies and partially defines the gentle fictionalisation of Stephen’s journal and the character of Annie.<br/> \n<b>Key words:</b> Biographical Practices, Canada, fictionalised biography, archive and field research, family history, practice as research, home children, first world war.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".