Saying What You Do and Doing What You Say: The Performative Dynamics of Lean Management Theory
Bibliographic record
Abstract
Why are certain theories able to impose themselves and influence organizational practices in a significant way? Rooted at the intersection of inquiries into management fashions and into performativity, we investigate the case of the Québec public health care system, where a managerial theory – that of “lean\nmanagement” – has recently emerged, gained saliency and become dominant in organizational practice. Adopting a longitudinal and multi-level research approach, we focus more precisely on the conditions that allow performativity to\noccur and increase, considering how this process unfolds over time. We therefore study the processes and the conditions through which lean management theory imposed itself, both in the global health care system and in two distinct health care organizations and the processes and the conditions through which this theory, while imposing itself, constructs a reality for these organizations, eventually reinforcing the theory itself. By unveiling the action of three performative dynamics in this particular case, our study provides a reflection on the catalysts and inhibitors of performativity, that goes beyond the specific case and that could be relevant to researchers interested by performativity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.093 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".