Bibliographic record
Abstract
PHIL 2001 (Introduction to Logic) is a second year philosophy course that is open to first-year students. Interest in this course has grown in the past few semesters, with enrollment of approximately 350 students per semester. In this course, skill practice is essential for student success and this practice is in large part achieved through tutorials which are run by teaching assistants. However, the efficacy and interest in the tutorials throughout past semesters have been mixed, lacking in consistent results and delivery. As such, the project aimed to create a database of examples and template materials that the TAs can draw from, ensuring consistency across the sections. Drawing from my experience as a long-term TA for the course, I provided insight into the needs of students, as well as the challenges faced by the TAs in tutorials. Through a highly collaborative process, a variety of resources were developed to be shared and used in future semesters, including: a weekly curriculum for tutorials, an overview of key concepts to be covered in tutorials, a problem bank featuring examples drawn from pop-culture, news, and philosophical texts, and a TA ‘best practices’ guide. These resources are highly transferable to future semesters and will improve both the quality and consistency of tutorials. Additionally, this model could be adapted for other courses with tutorial sections.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.053 |
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".