Bibliographic record
Abstract
Throughout the ages, magicians, scientists and charlatans have created life-like artifacts, some purported to be intelligent. In one famous case, the Chess Player, the intelligence was a little person hidden inside doing the thinking. Analogously, throughout the history of philosophy, and cognition, theories have arisen to explain intelligence in humans, but a philosophical problem with many such explanations is that they use what is called a homunculus argument – the explanation, upon scrutiny reveals a “little one” (homunculus) in the proposed mental apparatus that is responsible for thinking. For most of the era of computing, the Imitation Game, as so simply yet subtly put forward by Alan Turing, has been considered the gold standard for measuring this mysterious quantity, though recently Hector Levesque has pointedly argued that the time has come to abandon Turing's test for a better one of his own design, which he describes in a series of acclaimed papers. In particular, we argue that Levesque, who has cleverly found the ‘homunculus’ in the arguments of others, has essentially regressed the problem of intelligence to a homunculus in his own system.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".