Bibliographic record
Abstract
Writing creative stories from a blank page is a challenging task, particularly in the modern game industry where dozens of writers can contribute to the stories and settings of a constantly evolving artificial world.We introduce a system which recommends interesting, evocative, and thematically coherent words to help creators write thematically connected stories.We combine principles from human creativity enhancement and computational creativity to build a creative assistant based on word association research.We show that careful corpus selection, filtering based on emotional sentiment, and promoting remote associations through paragraph scale segmentation can produce recommendations that promote creative goals better than alternative word association algorithms according to our creative word indicators.iii I would like to begin by thanking my thesis supervisor, Dr. David Mould.His attention to detail, his patience, and his enthusiasm for this work was essential during this research.Regardless of the circumstances, his positive attitude and passion in our meetings would always leave me reinvigorated and ready to get back into the work.I could not have completed this work without his outstanding kindness, advice, patience, and support.His guidance has shaped my way of looking at computer science since my first year as an undergraduate student and has kept me driven to pursue this research.I would like to thank the thesis committee for reviewing my work and providing me with valuable suggestions.Their input has improved this thesis and has helped me improve as a researcher.I would also like
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".