Why Tracking Theories Should Allow for Clean Cases of Reliable Misrepresentation
Bibliographic record
Abstract
Abstract Reliable misrepresentation is getting things wrong in the same way all the time. In Mendelovici 2013, I argue that tracking theories of mental representation cannot allow for certain kinds of reliable misrepresentation, and that this is a problem for those views. Artiga 2013 defends teleosemantics from this argument. He agrees with Mendelovici 2013 that teleosemantics cannot account for clean cases of reliable misrepresentation, but argues that this is not a problem for the view. This paper clarifies and improves the argument in Mendelovici 2013 and responds to Artiga’s arguments. Tracking theories, teleosemantics included, really do need to allow for clean cases of reliable misrepresentation.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.008 | 0.044 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".