Bibliographic record
Abstract
Abstract This qualitative study explored the perceptions of students who were involved in the governance process of a case university. The focus of the study was to learn how the university context contributes, or not, to participation in student governance. More specifically, the questions that guided the study were: "How do students become involved in university governance?", "What were the kinds of governance activities in which they were involved?", and "What issues and/or challenges did the student representatives encounter, or not, while participating in governance?" The data sources included student interviews, professor interviews, field notes, and university public documents. Constant comparison analysis of the student interviews resulted in eight descriptive categories from which three major themes emerged. The first theme, "governing framework," included the complex arrangement of subcategories, disciplined governing, and executing responsibilities. This theme revealed the complex organization of university and student associations, their functions and the significance of their role in university governance. The descriptive sub-categories participatory factors, interactional factors, and personal propensity of students were merged into the "enticing involvement" theme, which helped anticipate students' participation in university governance processes and how the university could further support their involvement. Students' personal reluctance, role definitions and sense of belongingness established the conceptual theme, "defining the line." These themes reflected the overall university governance process and the corresponding student engagement status. Exploration of the governance process of a large Canadian university through students' eyes revealed many examples of "micro-aggressions". Such incidents of micro-aggression were not very evident to casual observers and people around students. Even though these micro-aggressions were not intentional, students faced problems that included, but were not limited to, gender-specific biases, racial biases, student-specific biases, and so forth. Students felt that such occurrences had occurred as they were inferior to any other group of university. Finally it had been suggested that mainstream students should be recognized as a specific type of stakeholder of university, which could counter the problems faced by students. Future research should explore the issue involving students, professors, administrators, and other stakeholders.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".