Academic Integrity Inclusivity and Accessibility Study: Research Project Brief
Bibliographic record
Abstract
Purpose: The main goal of this project is to assess the academic integrity, policy, procedure, and supports at one community college using multiple frameworks of equity, diversity, and inclusion (EDI), including principles of Universal Design for Learning (UDL), decolonization and Indigenization, and stress and mental health. Methods: A mixed methods approach to collect both qualitative and quantitative data is used to answer the research question. Academic integrity policy and procedure documents are qualitatively analyzed using current exemplar principles (Bretag et al., 2011b) and a tool created by the research team that assesses inclusivity, accessibility, decolonization, and mental health. Experiences of key stakeholders in the academic integrity process are collected using survey, focus group, and interview methods. It is our direct intention to empower academic integrity stakeholders through their voices, experiences, and their participation to drive change and to engage in community building. Data sources: Publicly available academic integrity policy and procedure documents and stakeholder experiences collected through surveys, focus groups, and interviews from one Canadian community college form the basis of the data for this project. Implications: The project is significant to the college specifically and to higher education institutions more broadly as the multi-framework tool, developed under Creative Commons license, may be used by policy analysts and practitioners to assess academic integrity processes toward reforming policy, procedure, and supports. The project also assesses teaching practices which may help identify stressors and gaps in support for administrators to address in their institutions.
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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".