TALEVAS model: an integrated quality methodology
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
Purpose The purpose of this paper is to combine systems thinking, lean management, value methodology and Six Sigma concepts into an integrated quality methodology using the TALEVAS model. Design/methodology/approach TALEVAS is an acronym for Tandem‐Lean‐Value‐Sigma, as each element correlates to a best practice or concept mentioned by intent. The model is based on two theories: “The rising pendulum system” and “The seven rules of quality driving” proposed in this paper. Findings Four key performance drivers are identified using the model. These are: communication, investigative correction, innovation, and synchronization. Practical implications The integrated methodology can be deployed by any type (product‐or‐service based) or level (small, medium or corporate) of an organization in order to gain a competitive advantage in the market. Further, there is a possibility that recent cases of product recalls could be reduced or avoided by companies through implementing a TALEVAS Quality approach. Originality/value The paper displays the interdependence between the quality concepts by model analysis. This reflects a more holistic approach to quality required by organizations to raise the bottom line, reduce costs, promote value, and provide consistent products to customers.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it