Bibliographic record
Abstract
Over half a century ago, Alan Turing proposed "the Imitation Game" as a test of whether machines such as digital computers can be said to think. Subsequent discussion of Turing tests -human-machine interactions that are importantly similar to Turing's original Imitation Game -has been limited in its understanding of what they are good for, viewing them as good either for prompting philosophical reflection on the limits of our concept of the mental or for addressing empirical questions about the machines involved in human-machine interactions. This project is an attempt to expand our understanding of what Turing tests are good for: my novel proposal is that they are good for addressing empirical questions about the humans involved in human-machine interactions. More simply put, I argue Turing tests are useful not merely as conceptual prompts or nonreflexive experimental apparatus, but as reflexive experimental apparatus. I begin with an examination of Turing's own work and of the subsequent discussion's limited understanding of the usefulness of Turing tests as either conceptual prompts or nonreflexive experimental apparatus. I then lay out the key elements of my novel proposal that they are useful as reflexive experimental apparatus. Finally, I offer a "proof of concept" for this novel proposal by describing and discussing the results of one preliminary attempt to use Turing tests as reflexive experimental apparatus.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".