Bibliographic record
Abstract
ABSTRACT Our received theories of self-deception are problematic. The traditional view, according to which self-deceivers intend to deceive themselves, generates paradoxes: you cannot deceive yourself intentionally because you know your own plans and intentions. Non-traditional views argue that self-deceivers act (sub-)intentionally but deceive themselves unintentionally and unknowingly. Some non-traditionalists even say that self-deception involves a mere error (of self-knowledge). The non-traditional approach does not generate paradoxes, but it entails that people can deceive themselves by accident or by mistake, which is rather controversial. I argue that a functional analysis of human interpersonal deception and self-deception solves both problems and a few more. According to this analysis, my behavior is deceptive iff its function is to mislead; I may but need not intend to mislead. In self-deception, then, the self engages in some deceptive behavior and this behavior misleads the self. Thus, while it may but need not be intended, self-deception is never an accident or a mistake.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".