Bibliographic record
Abstract
Although many creative writers listen to music while they write (Chamorro-Premuzic et. al.,2009), it is not yet understood if background music enhances or decreases a writer’s creativity. Previous research suggests that the presence of music increases arousal, which in turn affects creativity (He et. al., 2017). Furthermore, people display higher levels of creativity when exposed to familiar music (Schellenberg et. al, 2007). However, many of these studies have examined music priming (when music is played prior to the task), as opposed to background music (when music is played during the task). There is also a lack of research on creative writing, especially short stories. In this study, experienced and novice writers will be asked to write two 300-word fictional stories with provided prompts. One will be written in silence, and the other will be written while listening to playlists the participants has created themselves. Participants will have fifteen minutes to write each story, and then they will answer a series of questionnaires that measure personality, empathy, and participants’ histories of creative activities and achievements. The stories written by these participants will then be read by two sets of raters: those who are also experienced in creative writing, and novice writers. The raters will compare each author’s stories, and judge which story is more creative. The hypothesis is that music will enhance the creativity in both groups of writers, but will have a greater effect in novices, as experienced writers are capable of being creative with or without music. Faculty Mentor: Kathleen Corrigall Department: Psychology (Honours)
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".