Creating and Managing a Repository of Past Exam Papers
Bibliographic record
Abstract
Exam period can be a stressful time for students, and having examples of past papers to help prepare for the tests can be extremely helpful. It is possible that past exams are already shared on your campus—by professors in their specific courses, via student unions or groups, or between individual students. In this article, we will go over the workflows and infrastructure to support systematically collecting, providing access to, and managing a repository of past exam papers. We will discuss platform-agnostic considerations of opt-in vs opt-out submission, access restriction, discovery, retention schedules, and more. Finally, we will share the University of Toronto set up, including a dedicated instance of DSpace, batch metadata creation and ingest scripts, and our submission and retention workflows that take into account the varying needs of stakeholders across our three campuses.
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 | 0.042 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.039 |
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".