Bibliographic record
Abstract
Communication is a powerful tool. We use it to share information, express how we feel, coordinate in cooperative endeavors, and so much more. However, we regularly use standard communicative devices like speech and gesture to piss each other off, to manipulate each other, and to cause harm; flipping you off or calling you by a slur is no less clear a way of sending you a message than is stating a simple fact. In roughly the last decade, philosophers of language have turned their attention away from idealized communicative contexts to develop new theories about non-ideal communication, which includes phenomena like lying, misleading, and deceptive speech. The philosophical study of lies and deception predates the discipline of philosophy itself, but contemporary theories place a special emphasis on their explanatory and moral applications in real-world social and political contexts. While care has been taken in this tradition to define lying speech, and to distinguish it from speech that is ‘merely’ misleading or deceptive, insufficient care has been taken to define and distinguish misleading speech as a distinctive communicative act. This gap in the literature overlooks a class of cases that are not captured by definitions of lying and deception and their unique moral significance. \nIn this thesis, I defend the following, novel account of misleading: \n \nMisleading:\t A communicative act C is misleading iff C has a tendency to cause others to reason badly \n \nI argue that this definition of misleading is the best way to capture the fact that misleading does not necessarily cause anyone to form a false belief. My view challenges prior accounts of misleading in the literature which take this element to be necessary, and thereby fail to accommodate cases of misleading that are not successful or result in something other than a false belief.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".