Prize culture: creative ecosystems in the attention economy
Bibliographic record
Abstract
This paper examines the proliferation of awards, prizes, and highly publicized honors in the creative industries as a flexible strategy for adapting the commercial creative ecosystems. Creatives thrive in ritualized status tournaments in which attention markets draw together creative suppliers, brokers, sponsors, and audiences into a loosely bounded ecosystem dependent on transforming reputation into intellectual capital. In the attention economy, creative ecosystems employ a prize culture strategy to manage and marry this ecosystem’s artistic with commercial components and to adapt the ecosystem to an uncertain future. What makes the prize culture strategy effective in the attention economy is its flexibility, public reach, and ability to direct attention in two opposing directions – prizing the established creative veterans in well-developed genres and anticipating emerging creatives. With this Janus-faced reach, a prize culture strategy enables creative ecosystems to establish shared intellectual property; the prize culture, a means of binding diverse participants, encourages an artistic-commercial marriage within the ecosystem while promoting past successes and anticipating new stars.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".