Bibliographic record
Abstract
We love creativity. Everybody loves creativity and everybody wants a “Culture of Creativity.” However, there is strong evidence that we do not even like creativity, especially under stressful conditions. Creativity thrives in conditions of uncertainty, vagueness of purpose and psychological discomfiture — conditions that can be unbearable when added to the current anxieties of a shrinking academic landscape, the pandemic, let alone wicked problems like the climate crisis. We are terrified in these traumatic circumstances, so we shrink away from creativity toward the safety of what is known, understood and proven. As a result, Proxy Creativity emerges — one that is tidy, easily processed and consumed. “Creative” educational tools like Design Sprints, Pithy-Themed Courses, Compelling Branding Platforms and Curated Campuses emerge because they feel safer. These tools “sell” Proxy Creativity to potential students, current students, faculty, as well as to those outside of art and design institutions. This is a raw deal. Creativity in its most primal, unwieldy and disruptive form is a valuable tool used in interdisciplinary teams that are addressing wicked problems. Proxy Creativity may be more comfortable right now, but it is a poor substitute. Are we biased against creativity?
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.024 |
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".