Female Nightmare Protection as a Function of Sex Role Identity and Sex of Experimenter
Bibliographic record
Abstract
Previous research has shown that video-gamer's dreams are associated with less threatening content but also that they often do not consider such dreams to be nightmares, or find them to be scary. It is likely that gamers who play combat-centric action-type video games practice quick reactions that allow them to develop defensive maneuvers, so that when the gamer experiences a chase-type threat in a dream, it is empowering instead of intimidating. The nightmare protection effect has been demonstrated in several correlational and experimental studies, using military-personal and university-age males as participants. With female gamers, the effect is more likely to be present in those that play combat games with high frequency and who identify as more masculine in their sex-role identity. Addressing the limitations of a previous experiment with male-gamers, current and ongoing research is experimentally investigating the nightmare protection effect in university women, as a replication and extension. The current study has two independent variables (computer use and sex of experimenter). The computer use conditions are combat video game play and computer scholarly search task, which serves as a control condition. All research participants are exposed to a frightening film clip and are randomly assigned to play a first-person shooter combat-centric action video game or perform an online computer search task. They are randomly assigned to either a male or female experimenter. We hypothesized that participants playing an action first-person perspective video game, and who identify as more masculine in their sex-role identity, will experience the nightmare protection effect. Discipline: Psychology Faculty Mentor: Dr. Jayne Gackenbach
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".