A Collaborative Game to Assist Veterinary Undergraduate Students in the Conceptualization of a Research Dissertation
Bibliographic record
Abstract
The veterinary thesis, mandatory in many veterinary curricula, represents an important step in terms of students' autonomy and research-led education. The choice of the topic is crucial, especially when the student initiates it rather than accepting a teacher's proposal. To promote diverse thesis work and provide greater support to students, a collaborative game and preparatory activity have been designed with a focus on the thesis project. The game was developed to raise awareness among students about the challenges of creativity and feasibility. The training sequence began with a short presentation by the instructor about the ideation process and project management tools. Then, under the instructor supervision, the students played using a deck of cards and worksheets. In the first phase, each group of students drew four cards and used them to imagine an experimental or a clinical thesis topic. The cards represented the elements of a PICO-like question (Population, Intervention, Comparison, Outcome). In the second phase, the groups exchanged their forms with another group, allowing them to evaluate each other's work and suggest modifications. This evaluation phase was based on "De Bono's 6 thinking hats method," which encourages students to adopt different perspectives such as critical, creative, or objective thinking when assessing their peers' work. The student appreciation survey, conducted after the training, yielded positive results, particularly in terms of helping students identify different approaches to a topic and fostering collaborative peer discussions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".