‘It Was Such a Handy Term’: Management Fashions and Pragmatic Ambiguity*
Bibliographic record
Abstract
abstract This article builds on constructs that authors have labelledstrategic ambiguity,interpretative viability,umbrella constructs, andboundary objects, and suggests that these constructs all articulate a central concern for collective action and the role of ambiguity therein. It characterizes aspragmatic ambiguitythe condition of admitting more than one course of action, and elucidates and operationalizes this new construct. Drawing on the sociology of translation ( Callon, 1986 ; Latour, 1987 ), [1] it argues that pragmatic ambiguity is both the result and the resource of a collective process ofintéressementoccurring during the rise in popularity of a new management approach. Following Benders and van Veen (2001 ), the article posits that pragmatic ambiguity increases during the rise of a management fashion. It provides empirical evidence in support of this claim by means of a longitudinal analysis of quality management (QM) concepts as articulated by several authors both before and during the Quality Movement of the 1980s and 1990s. The analyses of QM texts show that concepts became vaguer, more ambiguous, and more general as the Quality Movement gained momentum, suggesting the presence of a positive feedback loop between pragmatic ambiguity and popularity. In addition, the data illustrate how pragmatic ambiguity was achieved and sustained textually, and how it was supported by a variety of social, linguistic and rhetorical factors.
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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 | 0.008 | 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 | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".