Doing Jurisdictional Work: Organizations and the Nurturing of Professionalization
Bibliographic record
Abstract
There has been significant attention to professionalization projects, although the focus has so far been almost exclusively on how professionals themselves engage in actions designed to claim or extend their jurisdiction. We build on recent work exploring a potentially symbiotic relationship between organizations and professionals, to explain how organizational actors can encourage and facilitate processes of professionalization. Drawing on data from China, we studied the experiences of Community Health Centers (CHCs) as they participated in the development of the General Practitioner profession. We develop a conceptual model explicating three sub-processes that organizations can enact to support the efforts of new professionals as they attempt to realize the extent of their jurisdiction within workplace settings. By highlighting the jurisdictional work of organizational actors, we contribute to the literature by developing a more nuanced understanding of the symbiotic relationship between organizations and professionals. We also contribute to the literature by showing how organizations can provide opportunities for professionals to establish relational authority and claim jurisdiction as they engage in the co-production of professionalization projects.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.018 |
| 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".