Bibliographic record
Abstract
BEST business excellence addresses the issue of excellence and sustainability from four perspectives: bio/physical, economic, social, and technological. The concept of BEST business excellence seeks to address the balance of objectives that many academics and practitioners alike believe are necessary, perhaps not sufficient, to secure the long‐term survival, prosperity, and thriving of humankind and its institutions. Somewhat allied with triple bottom line, this concept is in its infancy and little work has been completed in the formation of the concept intuitively or formally. This paper begins a discourse to develop an optimization model for the concept of BEST business excellence. The models presented herein are graphical and descriptive and offer a basis for further development. These models represent the transformation from maximizing economic outcomes as the organizational objective constrained by B‐sustainability (bio/physical) largely through regulation, S‐sustainability (social) largely through a sense of obligation or by consumer action, and T‐sustainability (technology) largely through the limitations of current technology available. The new model offers the different perspective of the objective function containing variables representing B‐, E‐, and S‐sustainability, wherein those objectives are jointly optimised using technology (T‐sustainability) where cost becomes the constraint. The resultant descriptive model shows how technology forms the centerpiece of optimization and provides direction for technological development resulting in simultaneous optimization of bio/physical, economic, and social objectives.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".