What is an Artifice? The Precarities of Culbertson’s Two Distinctions on Generative AI
Bibliographic record
Abstract
Culbertson's recent paper within the Journal of Applied Hermeneutics offered two distinctions at work in the reading and understanding of Natural Learning Processing. This paper was a significant articulation of a general hermeneutic response to the prospect of generative AI and its challenges for interpretation. But it also raised some nagging questions on whether there is a risk that we settle too quickly on the promotion of close reading and the aspirations of “thinking with others” in dialogical open-ness, and in doing so also settle a little too quickly on what the object of the hermeneutic encounter is, at the expense of other possible dialogues, or traditions, at work? This paper argues that a dimension at work in the debate over generative AI often missed from hermeneutic discussions is that of the artifice. It the dimension of the artifice, as an interpretative element of the “artificial” at work in AI; not as a critique of Culbertson’s two distinctions, but rather to suggest a certain precarity to their resoluteness, a precarity which further research would benefit from.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.103 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| 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; 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".