Explaining the Puzzle of Homeless Mobilization: An Examination of Differential Participation
Bibliographic record
Abstract
In this article, the authors examine participation in protests about homelessness by an unlikely set of participants—the homeless themselves. Through an analysis of data derived from 400 structured interviews with homeless individuals in Detroit, Philadelphia, and Tucson, the authors examine why and to what extent some homeless individuals, and not others, participate in movement-sponsored protest activities. In addition, the authors assess the degree to which the factors that affect participation in this population align with previous research on participation in social movements generally. They find that certain characteristics of the homeless population reduce the importance of social ties with other homeless individuals in the recruitment process and that, contrary to what much past work would lead one to expect, homeless individuals who are less biographically available are more likely to engage in protest activity. In addition, strain, which is often not a significant predictor of engagement in other populations, is an important predictor of differential participation among the homeless. This study highlights features of the homeless population that yield somewhat different correlates of participation than found in most movement participation studies and, in turn, cautions against presuming an overall model of participation that explains the engagement of all groups in the same way.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".