Plagiaruedo*: teaching of academic integrity through a ‘whodunnit’ game (*any likeness to other games is intentional!)
Bibliographic record
Abstract
An academic crime has been committed – someone has been caught plagiarising! Did Prof. Crastinator forget quotation marks due to poor time management, or did Larry Lastminute deliberately cheat by submitting text generated by artificial intelligence (AI)? This workshop invited delegates to play ‘Plagiaruedo’, a board game designed and used to raise students’ awareness of academic integrity. In the game, participants visited departments of the University of Portsmouth, tasked with figuring out who plagiarised, how they did it and why they did it, before submitting their answer to ‘Turnitin’ … but beware – an incorrect answer meant failing the assignment! Academic integrity is often regarded as a serious topic, making it potentially challenging to teach without resorting to dry or even punitive materials. Through Plagiaruedo, presenters hoped to challenge traditional teaching methods and play with a subject matter that is not traditionally played with (Sicart, 2014), creating an open learning environment that encourages students to try something new (Whitton and Moseley, 2019). Presenters reflected on experimenting with their Learning Development (LD) practice and finding that play has purpose within higher education (James, 2019). Following the game, delegates were asked for feedback on using Plagiaruedo as a catalyst for subsequent academic integrity activities, before the presenters shared their own in-class examples. Feedback from this ‘playtest’ will help improve future iterations of Plagiaruedo. Playfully-minded colleagues had the opportunity to join presenters for a potential research project about perceptions of the game, to enhance the evidence base for playful learning in higher education.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".