Identifying Pathways to and Experiences of Street Involvement Through Case Law
Bibliographic record
Abstract
This research explores what can be learned about the experiences of streetinvolved people by reading cases that deal with people characterized on the record as "homeless." The author builds on existing empirical research by reading a large body of cases to discuss pathways to and experiences of street involvement. She proceeds to more closely explore cases regarding people (1) who are identified in the cases as homeless, and (2) find themselves before the courts for having engaged in income generating activities. The author argues that cases constitute knowledge about street involvement in ways that may take us beyond what we already know from existing research. They can, for instance, reveal how cascading and compounding disadvantages are navigated on a daily basis, point to some of the ways in which people attempt to exhibit resiliency in the face of these experiences, and record how legal processes operate to stifle or hinder such expressions ofagency.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".