The rise of generative AI and enculturating AI writing in postsecondary education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it