Bibliographic record
Abstract
This article proposes that the rise of GPT technology presents an opportunity to initiate meaningful discussions in the postsecondary classroom about the connections between writing, language, and personal autonomy. Partly grounded on predictive text, GPT-produced language is often recognizable by its blandness and its proneness to the predictable turn of phrase—qualities that postsecondary students (among others!) often struggle to overcome in their own work. George Orwell famously described relying on cliché as akin to turning oneself into a machine. The analogy arises from the lack of relationality in cliché-riddled writing, a quality similarly found in AI-generated text. Rhetoric and composition theory provides insights into the relational nature of written discourse and, equally, into the places where GPT technology falls short of the profoundly intersubjective and interpersonal elements underlying written communication. Foregrounding these findings in class discussions of GPT tools is a central task in training students to engage critically with such tools. Assignments inviting students to contextualize themselves as writers—linguistically, culturally, discursively—represent an actionable step to help students identify the relational and interpersonal contexts to which GPT output cannot attend.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".