MICRO-PROCESSES OF FIELD CONSTRUCTION: EVIDENCE FROM A GLOBAL LAW FIRM First draft, please do not cite without author’s permission
Bibliographic record
Abstract
during my stay at the University of Alberta. Namrata Malhotra and Marc Ventresca have provided helpful criticism and comments. Especially, I am indebted to my supervisors Laura Empson and Tim Morris for their valuable advice and guidance throughout the project. 2 Organisational fields represent a central concept in institutional theory and discussions of professional work. Notwithstanding the concept’s recognised importance, however, empirical definitions have remained vague. Field formation in particular still presents institutional theorists with an under-investigated puzzle. This paper addresses this puzzle by exploring micro-processes of field construction in the daily practice of English and German lawyers from a global law firm. Based on pilot study interview data, it identifies three principal work activities through which lawyers construct their fields: the drafting of legal documents, observations of senior colleagues, and negotiations of appropriate problem-solving approaches. The paper further examines how the nature of lawyers ’ work in different practice-groups affects the
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".