Bibliographic record
Abstract
Artificial intelligence (AI) tools, especially generative tools based on large language models (LLMs), such as ChatGPT, raise critical concerns for academic integrity, for ensuring genuine assessment of student learning, and for equity. Public understanding of these tools is clouded by hype about their capabilities, as they are often treated as knowledgeable and even sentient, and thus suitable for any human task. Of particular concern for instructors is how, and how much, students rely on these tools to complete their coursework. We address some of these issues in our classrooms by reporting on a recent pedagogical initiative within the Department of Linguistics at the University of Toronto during Summer 2023. As part of the initiative, we highlight the crucial role that linguistics can play in these discussions, by shifting the focus to LLMs as objects of study that are directly relevant to the linguistics classroom and to educate students on what linguistic tasks they are and are not good at. We offer strategies and sample assignment questions to help instructors deflate AI hype and facilitate greater AI literacy by demystifying the technology.
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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 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".