The rhetoric and reality of Lean: a multiple case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we analyse the similarities and differences between the descriptions of Lean found in the extant literature and how it is applied in practice. Using a multiple case study with seven cases from different sectors, we offer seven propositions about Lean as applied in reality and its relation to descriptions in literature. Our results indicate that organisations adopt the general rhetoric, and repeat the message conveyed by Lean proponents, in terms of the rationale for, and expected outcomes of, applying Lean. Furthermore, we see that the decision to implement Lean often precedes the identification of problems in the organisation, which causes a risk of an unfocused change process. Lean initiatives also tend to have a rather narrow scope, which contradicts the holistic view advocated in the literature. This, together with the variation in operationalisation, makes it difficult to predict the outcomes of a Lean initiative. Our study suggests that our findings do not depend on organisation size, sector or industry.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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