Anchoring into Space: A Reflexive Approach for Attending to Trauma When Engaging in Research with Older Persons with Experiences of Homelessness
Bibliographic record
Abstract
Reflexivity, which requires the conscious appraisal of how researchers’ social positions and subjectivities interact with the research process, has become increasingly popular in qualitative research with participants at heightened risk of marginalization and trauma histories. Despite the documented traumas associated with marginalization, little has been written about the process of integrating a reflexive approach into research with marginalized communities. This methodological paper seeks to redress this gap by illuminating how our research team used the lens of space as a reflexive framework to attend to positionality, transformation, and power in a qualitative study with older persons with experiences of homelessness. These reflections emerged from our work conducting research in one of three sites associated with a pan-Canadian study, on housing, aging, place/space, and homelessness. More specifically, they emerged from our team’s observations and de-briefings during and following data collection with 11 participants (aged 50+ years) of a long-term transitional housing site in Montreal, Canada. These reflections illuminate how integrating concepts of space may provide an avenue for attending to reflexivity when conducting research that informs policy and public service initiatives for marginalized communities, and support research processes that disrupt tendencies to overlook trauma.
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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 | 0.256 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.028 | 0.109 |
| Scholarly communication | 0.030 | 0.023 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.007 | 0.016 |
| 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".