The public library and intersections of houselessness in two small cities of interior British Columbia
Bibliographic record
Abstract
This thesis investigates how public libraries presently mitigate intersections of houselessness in two small cities in the southern interior region of British Columbia (B.C.). The research identifies on three main topics relating to intersections of houselessness, small cities, and the public library. The first topic seeks to understand the role of political economic systems (primarily, neoliberalism) as they exacerbate intersections ranging from the toxic opioid supply to housing and privatization, and anthropogenic climate change. The second topic outlines how these intersections and topics function in the small city landscape, broadening the research away from strictly urban environments. Lastly, the topic of the public library places an emphasis on infrastructure, both social and physical, and conceptualizes the public library as more than just a third space, but rather as a lived space. This centers a discussion around public libraries existing in opposition to privatized spaces within neoliberal society. This thesis follows an interdisciplinary research design, using two methodologies ‘content analysis’ and ‘semi structured interviews’ to deliberate five key findings. Content analysis reviewed sources from City Profiles and relevant Institutes, and highlighted key words within their respective discourse on the chosen topics. Semi-structured interviews were conducted with ten participants from the two sites of study for this thesis – Nelson and Vernon. Outcomes of this thesis position the state of small cities through the changing face of houselessness, urbanization and housing, and community efforts and actions. The findings also explore the function of public libraries as they service to promote inclusivity, safety, community, and harm mitigation. Moreover, this work addresses the role of librarians and library staff alongside challenges and barriers, with outcomes situating relational engagement, training, de-escalation, and skills equipping. Ultimately, these conclusions outline best practices to effectively achieve the overarching argument of the small city public library as a site of resistance. Through this investigation of the public library, we may be better equipped to understand intersections of houselessness in other small cities of B.C. and beyond.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".