Evaluating High Performance the Evidence-Based Way: The Case of the Swagelok Transformers
Bibliographic record
Abstract
Many of the publications on achieving high performance have been written by North American researchers and consultants, and the case companies they described originate mainly from the United States. However, there is a lack of long-term studies that subject the described techniques to rigorous evidence-based management research in North American companies, to test the ideas in practice over a period of time to evaluate their relevance to managerial practice. In this article, we evaluate the high performance organization (HPO) Framework, a scientifically validated technique for helping organizations become high performing, in the North American context. This framework evaluates the strengths and weaknesses of the internal organization of a company, using a questionnaire. This questionnaire was applied in 2013 at seven Swagelok locations in the United States and Canada. From the questionnaire improvement opportunities were identified on which the locations subsequently worked. In 2015, the questionnaire was repeated to evaluate the effects of these improvements on the locations’ performance and to identify the most effective interventions. The study results show that the application of the HPO Framework had different outcomes depending on local circumstances. Some locations experienced a growth while other locations used the framework to battle the consequences of adverse economic circumstances. All locations agreed that the HPO Framework had been instrumental, in a positive way, to the development of their organization and its people.
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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 | 0.109 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".