MétaCan
Menu
Back to cohort
Record W4411692997 · doi:10.1177/00018392251343515

Art for Whose Sake? Managing Professional Autonomy and Empowered Clients in the Porcelain Capital of China

2025· article· en· W4411692997 on OpenAlexaff
Siyin Chen, Marlys K. Christianson, Chen-Bo Zhong

Bibliographic record

VenueAdministrative Science Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutonomyChinaCapital (architecture)SociologyBusinessPublic relationsPsychologyPolitical scienceManagementEconomicsArtVisual artsLaw

Abstract

fetched live from OpenAlex

Existing research suggests that experts often protect their professional autonomy by rejecting lay clients’ feedback or passing it to intermediaries (e.g., managers and agents). However, the rise of review platforms and disintermediated marketplaces has empowered clients to publicly share challenging feedback, and experts’ defensive tactics may further erode public trust in their services. In contrast, our qualitative study of 67 porcelain artists in China reveals that experts can effectively translate clients’ feedback to preserve their professional autonomy. These artists decomposed and distilled their expertise—differentiating the essential, identity-defining aspects from the more-peripheral, expendable ones—allowing them to incorporate clients’ feedback into the latter aspects while retaining control over the former ones. This strategy enabled the artists to integrate client-driven creations into their professional identity as artistic experts, thereby preserving their professional autonomy. Notably, not all artists adopted this strategy. Those who considered their work as an indivisible whole were financially compelled to bend to clients’ demands, or they chose to exit the profession. These findings present a paradoxical view of professional autonomy, suggesting that experts can maintain their professional freedom by granting clients limited and selective influence, thereby fostering clients’ compliance and public recognition in an era of increasing influence by lay audiences.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.015
GPT teacher head0.339
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdministrative Science QuarterlySame topicDigital Economy and Work TransformationFrench-language works237,207