Feminist and Community Psychology Ethics in Research with Homeless Women
Bibliographic record
Abstract
This paper presents a feminist and community psychology analysis of ethical concerns that can arise throughout the process of doing research with women who are homeless. The unique contexts of the lives of women who are homeless demand that researchers redefine traditional ethical constructs such as consent, privacy, harm, and bias. Research that fails to do this may perpetuate the stereotyping, marginalization, stigmatization, and victimization homeless women face. Feminist and community research ethics must go beyond the avoidance of harm to an active investment in the well-being of marginalized individuals and communities. Using feminist and community psychology ethics, this paper addresses some common problems in research with women who are homeless, and argues for the transformation of research from a tool for the advancement of science into a strategy for the empowerment of homeless women and their communities.
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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 | 0.069 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.096 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".