Bibliographic record
Abstract
Universities across the country employ special constables as part of their efforts to create safe campus environments. These officers engage in a variety of responsibilities, and are often the first to respond to students, staff, and faculty experiencing safety concerns. This thesis examines the role of special constables in Ontario post-secondary campuses and their perceptions of the work they do on campus. Interviews with forty special constables employed across Ontario universities garnered a glimpse into the type of work that campus safety officers respond to, the value they perceive of the work undertaken, the impression they believe others have of them as special constables, and their interactions with students while addressing issues of safety and security on campus. While campus crime management (including behavioural concerns related to safety) relies upon campus safety, few studies focus on the those directly responsible for addressing crime on campus (Allen, 2015). Findings illustrate that there is a lack of understanding of the work undertaken by special constables, and a perceived lack of respect and support. Theories that guided the research included Human Development & Capabilities Approach, student development theories, and critical race theory. Aspects of the Capabilities Approach, such as what one achieves and how they are able to garner achievements, along with what one values as part of their well-being and whether the social environment supports well-being (Sen, 1997) are used to understand the officers’ motivations, values, and preparedness for the role they hold. Student development theories provide insight into the campus student community, those key actors with whom officers interact, and the impact of these interactions. Finally, critical race theory is used to examine policing as a research theme both from the perspectives of those who are policed and those doing the policing. In particular, it provides insight into the narratives of Black officers working as special constables in a post-secondary environment as well as issues of intersectionality and oppressions experienced by officers.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".