Reflections on ‘doing’ participatory data analysis with women experiencing long-term homelessness
Bibliographic record
Abstract
This paper draws on the lessons learned from the [in]visible project, a community-based research partnership that aimed to learn more about the experiences of women, without children in their care, who experience chronic homelessness in Hamilton, Ontario. Through involving 70 women as participants, we used narrative and arts-based research methods to learn about the experiences and housing needs of this population. The purpose of the [in]visible project was to involve women in identifying gaps in housing services and generate recommendations on how permanent housing for women should be developed. This paper demonstrates how the participation of the women in three distinct data analysis activities including arts-based think tanks, participatory theorizing, and the creation of a conference workshop supported the participation of women at all stages of the data analysis process. This paper contributes to limited scholarship on participatory data analysis by presenting pragmatic and low-barrier ways of ensuring the findings and the direction of advocacy efforts reflect the social justice and change priorities of the women involved.
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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 | 0.114 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.059 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.013 |
| 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".