Bibliographic record
Abstract
The nature of creativity has long been a contested field of beliefs and ideas. Theorists make claims based on a wide range of positions developed through educational practices. Various disciplines have particular views of creativity, artists claim knowledge of creative practice, philosophers provide substance to support ideas, while psychological attributes are posited to affirm the nature of creative learning. Creativity lends itself easily to individuals who make statements that reflect on the nature of both creating and the experiences that enable individuals to create. This edited collection of research and practice explores this range of perspectives in three parts through a variety of disciplines, global contexts, and practice in higher education. Part I: Creativity Authors: Belinda Allen; Arianne Rourke; Rosemary C. Reilly; Candyce Reynolds, Dannelle Stevens & Ellen West; and Kylie Budge. Part II: Teaching Creatively Authors: Natalie Senjov-Makohon; Tiina Moore; Jenny Ramirez and Howard Sanborn; Nicholas McGuigan and Thomas Kern; Patsie Polly, Julian Cox, Kathryn Coleman, Jia-Lin Yang, Nicole Jones & Thuan Thai; Mary A. Burston; Dannelle Stevens, Candyce Reynolds & Ellen West; and Megan McPherson. Part III: Creativity in Practice Authors: Leela Cejnar; Bodil Rasmussen, Catharine McNamara & Anna Crow; Emily Frawley; Jia-Lin Yang, Kathryn Coleman, Mita Das & Nicholas Hawkins; Narelle Lemon; Lorraine White-Hancock; and Adele Flood.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".