Bibliographic record
Abstract
Drawing on General Strain Theory (GST), the research examines the effect of discrimination against individuals experiencing homelessness on offending behaviors. Operationalized as experiences with discrimination I test the direct and indirect effect of this strain through five mediators often overlooked in the empirical work on GST: anger, depression, low constraint, criminal peers, and criminal attitudes. The findings reveal little support for a direct association between homeless discrimination and general offending. Instead, the results indicate that homeless discrimination is associated with stronger anger, greater depression, lower constraint, more criminal peers, and escalated support for attitudes promoting offending. In turn, anger, low constraint, criminal peers, and criminal attitudes (but not depression) are related to higher levels of offending, with homeless discrimination impacting criminal behavior indirectly through these 4 factors. Crime-specific results suggest homeless discrimination has an indirect effect on all crime types through criminal peers. However, other indirect effects are limited to particular offense categories (criminal attitudes-property crime; anger-violent crime; low constraint-drug selling). The findings are theoretically contextualized, and suggestions for future research are offered.
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.
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 | 0.001 | 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".