<i>Sometimes you have to go under water to come up</i> : A poetic, critical realist approach to documenting the voices of homeless immigrant women
Bibliographic record
Abstract
Methodological debates concerning feminist research design tend to focus more on the process of data collection than on the process of data representation. Nevertheless, data representation is fraught with difficulties, especially in communicating research findings concerning vulnerable populations to diverse individuals and groups. How do feminist social work researchers represent the voice of the research participants to community and service organizations while simultaneously meeting the expectations of the academic or political institutions soliciting the research? In this article, we discuss how we approached this dilemma with data collected through a research study on immigrant women experiencing homelessness and housing insecurity. Guided by feminist methodological principles, we drew on the tenets of critical realist theory, integrating this analysis with poetic inquiry to reconstruct the women’s voices in the representations of research data. We discuss these modalities and provide two case examples to illustrate their application.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.056 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".