What is a Professional Service Firm? Towards a Theory and Taxonomy of Knowledge Intensive Firms
Why this work is in the frame
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Bibliographic record
Abstract
A growing literature on professional service firms is hindered by the ambiguity of its central concept, which leads to an overly-narrow empirical focus and to over-generalizations that may tout inefficiencies as best practice. To address this ambiguity, I develop a theory of the distinctive characteristics of professional service firms and their organizational implications. I identify three distinctive characteristics--knowledge intensity, low capital intensity, and professionalized workforce - with which I propose a taxonomy of four types of knowledge intensive firms - Classic PSFs, Professional Campuses, Neo-PSFs, and Technology Developers - whose varying degrees of professional service intensity predict different organizational features. The analysis highlights the danger of conflating the implications of professionalization with those of knowledge intensity and calls for comparative research across a wider range of professional services.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it