<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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it