Bibliographic record
Abstract
Two sets of content analyses were computed on 56 dreams of 27 hard core video game players gathered during semi-structured interviews in the winter term of 2006 at a Canadian college. The standard dream content analysis system from Hall and VandeCastle [19] was used to analyze these dreams as was another content analysis focused upon lucid/control dreaming. As expected gamers dreamt about gaming and indeed well over half of the dreams reported included easily recognized references to games. Since emotional regulation is thought to be a central feature of dreams, emotions of gaming which range from joy to anger and sadness were investigated in their social contexts in dreams with mixed results. Although gamers evidenced more self negativity in these dreams other indicates of positive emotional environments were present. If hard core gaming created distorted world views at a deep level of consciousness (i.e., in dreams) then this would be expected to appear in their dreams. However, despite the differences from norms, the overall picture is one of dreams reflecting game play while not dramatically distorting their emotional lives as depicted in dreams.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".