Patterns of special consideration requests at a UK university: reasons given and associations with demographic factors
Bibliographic record
Abstract
Students who are unable to complete an assessment due to circumstances beyond their control (e.g. illness) are often asked to submit a request for special consideration. However, few studies have looked at the reasons why these requests are made, or whether certain students are more likely to submit requests than others. The current study looked at 2126 such requests submitted by 461 students over one academic year and compared students who submitted requests with those who did not on several variables, including gender, full-time/part-time status and undergraduate/postgraduate courses. Distribution of these requests by type (e.g. physical health, bereavement) was examined, in addition to how many students submitted more than one request. The study found that around one-quarter of students submitted requests, with more than half of these related to mental or physical health issues. Full-time students were more likely to submit requests than part-time students although few other demographic differences emerged. The results suggest that some groups may be unevenly affected by special circumstances related to assessment although further work is needed to inform policies regarding special consideration.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".