‘Through my eyes’: feminist self-portraits of Osteogenesis Imperfecta as arts-based knowledge translation
Bibliographic record
Abstract
In this paper, we present an exploration of arts-based knowledge translation through photography highlighting the lived experience of Osteogenesis Imperfecta (OI), a genetic disorder. It forms part of our larger photovoice research project that involved six female photographers with physical impairment. This group of women shared their personal experiences through photographic stories to challenge pervasive, limiting negative attitudes and assumptions that surround disability. In this paper, we focus on the data, analysis and discussion to one type of impairment, OI, and two photographers’ work to present their:embodied expertise and knowledge of living with OI;self-portraits as contemporary disability identity, contributing to intersectionality in feminist and disability arenas;authentic voice as co-authors of this paper using Drew and Guillemin’s interpretive engagement framework;original arts-based research insights currently absent in the meagre qualitative research on OI. By presenting, analysing and interpreting self-portraits of OI as valuable arts-based knowledge, we hope to provide readers with a better understanding of disability and femininity as a pathway to greater inclusion.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".