Bridging Divides or Reinforcing Distance? The Interplay of Individual and Organizational Factors in Shaping Volunteers’ Relationships with Criminalized Women
Bibliographic record
Abstract
Abstract As governments cut funding for social welfare programs and shift toward neoliberal, marketized alternatives, non-profits have taken on a large and growing role in the provision of services to marginalized people. This paper examines how volunteers approach their relationships with service users in non-profits, as well as the consequences of and explanations for variation in their approaches. This research explores these questions ethnographically within Canadian non-profits offering social services to criminalized women. This paper offers three interrelated contributions. The first is a typology of different volunteer approaches within the penal voluntary sector—constructing volunteers as bystanders, tourists, visitors, or apprentices in their relationships with criminalized women. The second highlights how some of these approaches entrench social distance and inequality, whereas others encourage greater proximity and equity between volunteers and criminalized women. The third demonstrates how variation in volunteers’ approaches is the product of a dynamic interplay of individual and organizational factors. Together, these findings provide new insights about the conditions under which volunteers can do “good” or “bad” within non-profits. These insights could enhance the quality of volunteer work, reduce the reproduction of inequalities, and support the operation of organizations delivering vital services to marginalized people.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".