Art for whose sake? Occupational Identity, Commercialization, and Innovation in the “Porcelain Capital” of China
Bibliographic record
Abstract
When and how could experts translate the growing power of the audiences of their work (e.g., clients and customers), which existing research often characterizes as an occupational identity threat, into an opportunity for product innovation and identity reconstruction? I answer this question using a qualitative ethnography and a quantitative audit study of porcelain artists in China who have been forced to cater to market audience demands due to a market shift. I found that when facing challenging feedback from market audiences on their work, artists who distilled the core material element(s) of their work products that signify their occupational identity, and successfully experimented novel work products integrating their distilled material element(s) with audience feedback, innovated new artistic products and reconstructed their occupational identity. I further found that artists who did not go through the distilling and/or experimenting processes either quit the market or built strong segmentation between the work products that they created catering to audience feedback from their occupational identity through work product self-categorization, which helped protect their existing work and occupational identity. These findings contribute to a richer understanding of the role audience feedback, material objects, and innovation play in occupational identity processes, as well as the market implications of holding an unwavering occupational identity.
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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 | 0.001 | 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 | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".