Bibliographic record
Abstract
It is sometimes a difficult journey receiving ethics approval for research involving vulnerable populations, research involving our own children, or innovative research methodologies such as autoethnography. This autoethnographical account is a story about one student who wanted to write a PhD dissertation in a very different way and also the story of her co-supervisors who supported the student in using autoethnography as a creative way to share her “secrets of mothering” and who also supported her through an ethics-approval process that was both challenging and rewarding. This article reflects on a personal journey through the ethics-approval process at a Canadian university integrating components of the Tri-Council Policy Statement (TCPS), that guides university ethics committees across Canada, and asks the questions: What is the purpose of research and how can research ethics boards support research and stories that are difficult to tell and difficult to hear? It is an inquiry into secrets and difficult knowledge, and how reluctant we are to talk about difficult topics such as developmental disabilities, sexual abuse, divorce, accidents, and illness.
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 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 | 0.186 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.073 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.022 | 0.058 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".