Bibliographic record
Abstract
When we dream, we feel emotions in response to objects and events that exist only in the dream. One key question is whether these emotions can be said to be “essentially unfitting”, that is, always inappropriate to the evoking scenario. However, how we evaluate dream emotions for fittingness may depend on the model of dreams we adopt: the imagination or the hallucination model. If fittingness requires a match between emotion and evaluative properties of objects or events, it is prima facie plausible that dream emotions could fail to fit under the imagination model because it is unfitting to have an emotion toward an object we do not believe to be real. Under the hallucination model, dream emotions could be unfitting because their objects do not exist but we believe them to be real. More nuance, however, is required. By comparing dream emotions with the emotions we experience while imagining, engaging with fiction, and hallucinating, we conclude that although there are compelling arguments in support of the claim that dream emotions are essentially unfitting, these arguments are not entirely convincing, and it is more plausible that particular dream emotions can be assessed for fittingness under either model of dreaming.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".