The Link between Misinterpretation, Intentionality, and Mental Agency in the Natural Language Interpretation of “Fake”
Bibliographic record
Abstract
In formal semantics of natural language, an intersective interpretation works for many adjectives: x is a French lawyer iff x ∈ {x:x is French} {x: x is a lawyer}. For those adjectives for which this does not work, like “excellent”, we still have, at worst, a subsective modification ({x: x is an excellent violinist} ⊂ {x:x is a violinist}). Neither of these applies to “fake”, whose formal interpretation is a traditional challenge. In this paper, I propose an analysis of the semantics of “fake” in which the speaker’s attribution of intentionality (derived or original) to the object or person of which she predicates fakeness is central. In fact, the boundaries between the properties that ‘fake’ modifies and those it leaves unchanged are moved in function of this attribution of intentionality. In a famous 1994 paper, Dretske argues that for something to be specifically mental it does not merely need to exhibit original intentionality. It also has to be capable of misrepresentation, i.e. be a structure having a content independent of its causes. I argue that this intuition is implicitly contained in the natural language use of “fake”.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".