Caught in a machine that de-emphasizes human potential: Using Goffman’s theory of the total institution to understand service provider perspectives on boredom among unhoused persons
Bibliographic record
Abstract
Boredom has been identified as a factor affecting the lives of individuals during and following homelessness, yet no known studies have explored this experience from the perspectives of service providers. To address this gap, we conducted semi-structured qualitative interviews with 20 service providers working in shelters, drop-in programs, and housing services in two communities in Ontario, Canada. We analyzed our data using reflexive thematic analysis, guided by Goffman’s theory of the ‘staff world’ in his concept of the ‘total institution.’ The central essence characterizing our analysis was: Caught in a machine that de-emphasizes human potential. This essence is expressed through three themes: 1) “I think boredom is huge;” 2) “we just keep going back, and keep trying, and keep trying, and keep trying;” and 3) Housing is “…a shell that you could, with encouragement…potentially flourish in.” We conclude that the profound and pervasive boredom described in this research and in previous studies is symptomatic of broader structural problems created through inadequate responses to supporting individuals living with mental illness in our communities, contributing to rising and chronic homelessness. We argue that institutionalization of persons living with mental illness, which ended due to the neglect observed in such settings, has been replaced by an equally neglectful system of service provision taking the form of housing and homelessness services. We advocate for a system that not only provides basic resources for survival but also supports thriving through the provision of housing and opportunities for mitigating boredom through access to meaningful activities.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.064 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".