Mad and Disabled Realities Within Academic Integrity at the University Level of Education
Bibliographic record
Abstract
Researched in the study was the occupation of academic integrity/the handling of plagiarism at the university level of education related to students of the Mad and/or learning disability communities. My research questions were: a) “how have Mad students and/or students with learning disabilities experienced the handling of plagiarism?” and b) “how attentive are the academic integrity documents at Ryerson University to students of the Mad and/or learning disability communities”. A semi-structured interview collected participant data, while review of three documents collected textual data. A thematic analysis of this data revealed the negative experience of the handling of plagiarism and the unsatisfactory attentiveness of academic integrity at the university level, where ableism and disablism are implicated. Discovered amongst the data was the inacceptable and potentially harmful approaches at the university level for students of the Mad and/or learning disability communities, and the need for these to be remedied.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".