Tisser des liens pour aider des sans-abri : des relations de première ligne en action
Bibliographic record
Abstract
Objectives In a context where the rather informal ties between front-line practitioners can be conceived as a weakness to be formalized or a force to rely on, this paper explores and documents their conceptions on how they create, maintain and abandon their ties while helping homeless people.Methods Its empirical material is drawn from an interpretative case study on the liaison role of the Homeless Team (Équipe Itinérance). This case is based on semi-directed interviews with 35 practitioners (24 front line practitioners and 11 managers) from 13 Montreal organizations in the homeless sector. A thematic content analysis, mainly informed by the theoretical dimensions of relational work, was carried out on this material.Results Front-line workers report using different ways of creating (knowing each other, personalization, evaluation, organization of tasks and roles), pursuing (organization of tasks and roles, interpersonal reflection, maintenance, reparation) and interrupting (distancing, stopping, gradual withdrawal by an organization of roles and agreements) their ties.Conclusion Informal ties, allowing for flexibility and feedback, sometimes enable practitioners to carry out their tasks (quality of services, accessibility) while also dealing with the complexity of situations encountered by homeless peoples. The complexity of this relational work often makes it possible to cope with that of homelessness.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".