Living an ethical agreement: negotiating confidentiality and harm in feminist participatory action research
Bibliographic record
Abstract
Feminist community research is a collaborative, policy-oriented methodology that holds the promise of empowering the disadvantaged and building a more just society. But in the absence of critical analysis and the responsible use of power, this approach can lead to naive or even harmful practices.\n\nGrounded as they are in fieldwork, the interdisciplinary case studies in this volume acknowledge the real methodological and ethical issues that researchers can encounter as they negotiate contested research relationships. The authors discuss the strategies — successful and unsuccessful — that they have employed to overcome these challenges. The authors’ collective experiences working with diverse groups, from immigrant and Aboriginal women in Vancouver to poverty-reduction practitioners in Vietnam, reveal that truly equitable research projects require that we question core concepts and address crucial issues such as the promises and limits of reflexivity; the politics of place, time, and resources; ethical dilemmas and emotional responses; and the way issues of social justice, policy, and social change are embedded in research. -- From publisher description.
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 | 0.259 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.114 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.008 | 0.012 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".