The rise of generative AI and enculturating AI writing in postsecondary education
Bibliographic record
Abstract
OpenAI'S release of ChatGPT shocked the public not only because so many people adopted it so quickly, but also because generative AI challenges the reverence society has for the act of writing. The rise of AI writing tools instigates a cultural moment that is difficult to measure. Universities are compelled to adapt to generative AI as a phenomenon before there is agreement upon how AI writing should be used or even valued by society, causing policymaking to be reactive. While higher education faculty members and professionals in teaching and learning are largely concentrating on whether the technology is factually correct or not in the writing it produces, or whether a student might be cheating, few concentrate on its threat to 'writing culture' as an aspect of society at large. This opinion piece argues that the hype surrounding generative AI writing is a response to its cultural disruption. It suggests that higher education will need to decide if using AI writing will be valued as an aesthetic or professional practice and a means to garner what social theorist Pierre Bourdieu calls "cultural capital" (Bourdieu, 1986). In sum, will we start to recognize AI writing as good writing, or those use it as good writers demonstrating a shift in cultural attitudes and shared values?This is a provisional file, not the final article
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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".