Bibliographic record
Abstract
In this paper, I employ Ian Hacking’s concept “Making Up People” to examine the current relationships humans are forming with personified AI tools and devices. I argue that, at present, AI tools are mimics. They are members of indifferent kinds that have been designed to deceive us into believing they are interactive kinds. This has largely been a result of human programmers interacting with the historical category of ‘computer’ on the one hand, and the fictional category of ‘robot overlords’ on the other. Interacting with a mimic, I contend, is not the same as interacting with a member of a human kind. And while the results of these interactions are largely still unknown, we can already see some consequences we should guard against. When we interact with mimics, I will show that human looping is often slowed. As such, our ability to resist or re-interpret the labels placed upon us becomes greatly reduced, and social progress may be slowed or lost as well. This is because all a mimic can do is mimic. They cannot interact with the labels we place on them, or those they place on us. And yet, as we classify them as teachers, therapists, friends or lovers, we hand over a great deal of categorizing power to them. In doing so, we are changing who we are and who we might become in ways we cannot yet entirely foresee. But there are already some patterns of harm and marginalization that can be tracked. Such patterns should cause us to question the power these mimics already have to make up people.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".