An Examination of Institutional Structures, Policy, Narratives and Professorial and Other Stakeholder Perceptions and Experiences vis-à-vis Academic Integrity
Bibliographic record
Abstract
An Examination of Institutional Structures, Policy, Narratives and Professorial and Other Stakeholder Perceptions and Experiences vis-à-vis Academic Integrity Janet Joanne Shuh Doctor of Philosophy Department of Leadership, Higher and Adult Education University of Toronto 2020 Abstract This study explores the academic integrity mandate of a large multi-campus University in Ontario, Canada through the examination of faculty, staff, and administrator perceptions and experiences as well as the institution’s structures, policies and narratives. The study analyzed findings from three discrete data sources: institutional documents and structures; key informant interviews; and a faculty survey. The research questions and methodology drew from an emerging body of literature that has challenged researchers and practitioners to reframe their understanding of academic integrity from a “student” to an “institutional” (Bertram Gallant, 2016); “educational” (Bretag, 2016a; Fishman, 2016); and “academic literacy” (Howard, 2016) issue. Bolman and Deal’s (2003) four-frame model was used to explore the University’s approach to academic integrity through the structural, human resource, political, and symbolic “frames” as lenses for understanding organizational emphasis and leadership change vis-à-vis academic integrity. Faculty members’ experiences and perceptions were assessed, for the: prevalence of student dishonesty; salience of the underlying factors (individual student versus institutional/situational); and the impact of eroding integrity on core University functions, and the value of the four frames. The survey data were also analyzed for significant differences across the respondent characteristics of: academic discipline; primary campus of teaching; and length of teaching career.The study found that the University’s responses to academic integrity as well as the importance of approaches and considerations as assessed by faculty members were largely reflective of a structural lens. This was expected in that the structural frame (Bolman Deal, 2003) includes the central components of organizations, including “roles, goals, policies, technology, and environment” (p. 16) that are foundational to the post-secondary sector’s response to academic integrity concerns and/or opportunities. Key recommendations include creating more fulsome opportunities for academic integrity dialogue especially with students; acknowledging and mitigating inherent power imbalances; and incorporating symbolic and values-based strategies. The study also recommends aligning academic integrity more closely with the University’s quality assurance, research mandate, and institutional purpose; and fostering a commitment to continuous improvement of academic integrity policy, procedures, and governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".