The Influence of Curator Goals on Collections of Lived Experience Narratives: A Qualitative Study.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate how curator goals influence the design of curation processes for collections of mental health lived experience narratives. The objectives were (1) to characterize the goals of a range of curators of existing collections, and (2) to identify specific working practices impacted by these goals. RESEARCH DESIGN AND METHODS: Thirty semi-structured interviews were conducted with a purposive sample of curators of collections of lived experience narratives. Thematic analysis was conducted. Goals and impacts on working practice were tabulated, and narrative summaries were constructed to describe the relation between the two. RESULTS: Curators interviewed were from seven countries (Brazil, Canada, Hong Kong, India, Italy, UK, USA), and 60% had lived experience of mental health service usage. Participants discussed eight goals that inspired their work: fighting stigma, campaigning for change in service provision, educating about mental health and recovery, supporting others in their recovery journey, critiquing psychiatry, influencing policy, marketing health services, and reframing mental illness. These goals influenced how decisions were made about inclusion of narratives, editing of narrative content, withdrawal rights, and anonymization. CONCLUSIONS: Our work will support the development of curatorship as a professional practice by shaping training for curators, helping curators reflect on the outcomes they would like to achieve, and helping individuals planning a collection to reflect on their motivations. We argue that transparency is an essential orientation for curators. Transparency allows narrators to make an informed choice about donating a narrative. It allows policy makers to understand the influences on a collection and hence treat it as a source of collective evidence.
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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 | 0.057 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".