Professions, organizations and the state: Applying the sociology of the professions to the case of management consultancy
Bibliographic record
Abstract
In the recent literature on knowledge-based occupations it is frequently noted that some groups, such as management consultants, have been far less successful than others in developing a system of professional regulation and organization. This is generally attributed to the functional characteristics of their knowledge base, which is too elusive, fuzzy and perishable to sustain traditional professionalization projects. It is also suggested that these groups have little interest in becoming professions and have relied instead on alternative occupational strategies. In this article, drawing on a range of secondary sources, the authors highlight certain limitations of this account and offer an alternative. Focusing on the historical development of professional associations in the context of management consulting in the UK, the authors illustrate the role played by the state and large firms in undermining efforts to professionalize. A key contribution of the article is to highlight the need for a more inclusive approach to understanding why new knowledge-based occupations have failed to professionalize, one that gives more weight to the historical context and the role played by other key actors in shaping change.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.070 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 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".