Bibliographic record
Abstract
Organizations depend on experts to oversee and execute complex tasks. When faced with pressures to reduce their dependence on experts, managers encounter a control paradox: they require experts to explicate the very knowledge and discretionary approaches that are the basis of their control for the purpose of undercutting this control. Experts rarely consent to such a situation; therefore, attempts to reduce dependence on experts and control their work are more often aspirational than actual. Drawing on an ethnography of an organization that was required by a government agency to transfer the work responsibilities of experts to employees throughout the organization, this paper describes how a network of actors developed a discursive, political process to renegotiate control of expert work practices. Through censure episodes, long-standing and largely successful expert practices were examined one by one and relabeled as problematic in relation to established goals. The constructed breaches opened expert practices to evaluation, questioning, and eventual delegitimation within the organization. This process depended on the introduction of new roles that revised dependencies and generated new resources. This paper contributes to the understanding of control in organizations by theorizing how the emergent, symbolic work of censure episodes are a means of gradually subverting expert control. Further, these struggles are reconceptualized as multiple-role negotiations rather than bilateral manager–expert struggles.
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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".