Bibliographic record
Abstract
When we refer to something as automatic in ordinary language, we tend to speak of it as unconscious and working by itself —machinic, repetitive, needing no intervention or control from others to move along its natural course. If a process is automatic, we regularly assume that it happens independently of the human will. What is automated, in other words, will go on until non-human physical constraints prevent it from further labor, such as when the battery is dead in the robot or when the electricity goes out as the washing machine is running its usual course, or when one of its parts is worn out and needs repair. But if the machine “decides” that it is too tired or having a moody afternoon and wants to stop working mid-way through a task, we can’t help feeling very alarmed. Usually, we see automatism as precluding autonomy. Its automatic nature seems to suggest that it is, or ought to be, heteronomous in the sense that its course of action remains the same until it is told otherwise, e.g., when someone else turns the switch on or off. The contrast between the two statuses is prevalent in philosophical discourses as well, notably Descartes’ thought experiment that an automaton designed to look like an animal would be hard to distinguish from the real thing, but a machine that imitates humans would be far easier to detect, due to the latter’s language and general reasoning abilities, which reflect the fact that it is guided by immaterial mind.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".