"Playing with monopoly money" : a participatory exploration of the space-time aspects of homeless social capital
Bibliographic record
Abstract
Social capital is generally understood as the benefits received through people’s relationship with others. Although social capital is recognized as a fundamental ingredient in the psychological and material wellbeing of all people, little is known about how it functions for the homeless. Even less is known about context-sensitive accounts that consider the relevance of space and time in the production of social capital, the connection to the construction of homeless identity, and how social capital experiences of homeless people vary based on different factors, including gender. My doctoral research is an exploratory, participatory investigation of the space-time aspects of homeless social capital and the relationship to the trajectory of homelessness. It furthers theoretical and practical knowledge of homelessness, social capital, and the influence of gender through the use of Bourdieusian social capital theory and multiple methods and data sources. My research was conducted in Kelowna, BC, a mid-sized Canadian city, and involved one-year of fieldwork with time spent on city streets in locations known to be pivotal hubs of homeless activity. I conducted participatory mapping on an individual-basis with 29 street homeless adults, and on a group-basis with participants separated through a female/male binary. I used an advisory committee composed of formerly homeless people to guide research and interviews with key stakeholders with knowledge of street homelessness locally to inform my research approach. As a way to enhance policy and service recommendations, focus groups with key stakeholders and the advisory committee for the project were used to leverage research findings. Three key areas of findings were identified: 1) six distinct categories of homeless social capital with defined geographies, temporal aspects, and gender profiles; 2) three categories of fixed or variable factors with gender being the most important in shaping the space-time aspects of homeless social capital; and, 3) key themes from the thematic analysis of qualitative data, including the prevalence of negative social capital, the conscious performance of representations of homeless identity, and pronounced gender differences in the space-time aspects of social capital, including gendered survival and resistance strategies. The many theoretical, methodological, and practical implications are discussed.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.035 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".