Daily events and dream content: Unsuccessful matching attempts.
Bibliographic record
Abstract
Event descriptions (ED) from 6 different days and 6 corresponding morning dream reports (DR) were obtained from 13 participants. In a within-participant matching task, 14 untrained undergraduate student judges attempted to pair 6 EDs to 6 corresponding DRs for each of 6 participants. In a between-participant matching task, the same judges attempted to match 6 EDs from different participants to their respective DRs. For the within-participant task, a significance test for a single mean indicated that judges were unable to match dreams to their corresponding daily events at better than chance levels. For the between-participant matching task, however, it appears that judges were able to make pairs at significant levels but were still making on average less than 2 out of the possible 6 pairs per item. In a ranking task, two different judges read 1 ED and 6 DRs and then ranked the dreams from 1 to 6, 1 being most likely to be related to the ED and 6 being the least likely. Statistical tests revealed that dreams did not obtain better ranks (closer to 1) when they were the correct match than when they were not. These data appear to demonstrate that independent observers are unable to detect a clear resemblance between participants' daily events and manifest dream content.
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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 | 0.009 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".