Managers’ identities : Solid or affected by changes in institutional logics and organisational amendments?
Bibliographic record
Abstract
This paper studies doctors in Norway and Finland to compare how identities among professionals in managerial positions were expressed after changes in management in the aftermath of ‘New Public Management’ (NPM) reforms. Studying shifting identities provides a basis for investigating how institutions have changed and illuminates how agents within an organisation have implemented NPM-inspired reforms. Data from both countries revealed three groups: the majority of doctors/managers, who had a strong managerial identity; a smaller group who mainly identified as doctors; and a few doctors who displayed hybrid identities. Work experiences have a strong effect on how identity is perceived. Doctors who hold on to their professional identities seemed uneasy with their skills and ability to perform the tasks related to their new position. Many of the doctors were found to have altered their identities due to organisational amendments and the expanded focus on management-related issues. Hence, this paper concludes that a strong intervention in the sector from central government, as seen in Norway, has resulted in implementing general management to a larger degree than in Finland, but in a more hybrid manner. This is expressed through a focus on management, the institutional logics at stake and doctors’ identity formation.
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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 | 0.007 | 0.016 |
| 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 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".