Discovering dimensions of research ethics in doing oral history: going public in the case of the Ghent orphanages
Bibliographic record
Abstract
In this article, we argue that research ethics in the doing of oral history research are inadequately addressed in the existing body of research. Although oral history researchers have paid considerable attention to procedural ethical issues, there is currently a lack of attention on situational research ethics in the doing of oral history. We address particular ethical challenges that we experienced while reconstructing the history of three remaining orphanages after the Second World War in the city of Ghent (a city in Flanders, the Dutch speaking part of Belgium) by drawing on oral history research from former orphans and ex-staff members. Their rather surprising, yet pertinent, questions enabled us to discover the political nature of research ethics, and prompted us to engage in ‘going public’. We discuss the complexities of our attempt to provide a ‘questionable’ historical interpretation for the ambiguous history of these childhood institutions in the recent past.
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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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.094 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".