Professional judgment and legitimacy work in an organizationally embedded profession
Bibliographic record
Abstract
Abstract Professions have been traditionally understood as an alternative way of organizing work that stands in opposition to the corporate or bureaucratic organizational form. Increasingly, however, corporations are seen to be the source of new forms of expert knowledge and occupational categories. Yet we have little understanding of how expert judgement forms and is legitimated inside a large organization. In this study, we examine the emergence of standards of professional judgement in a government organization. Using archival and interview data between 2000 and 2012 we examine how experts in the Danish Film Institute generated professional standards of decision making against the backdrop of intense bureaucratic control. Our analysis demonstrates that norms of professional judgement emerge in a process that is inextricably linked to the emergence of professional role identities. Our core theoretical contribution is the discovery that the legitimacy work of managerial professions operates in two spheres; by first grounding claims of professional legitimacy in broad societal norms, and second, by grounding claims of professional identity in localized but increasingly abstract expressions of professional expertise.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".