Derivation Process of Vision That Bind the Overall Business Performance
Bibliographic record
Abstract
This paper tried to unbundle three important factors related to vision: (1) what is the true definition of vision, (2) how to develop an implementable vision and (3) how to relate the vision to the overall strategy. The study performed series of comprehensive literature reviews from 40 published manuscripts while using an explanatory approach to explain the stated research question. From these steps, this study found that vision is not only a dream, but more to achievable dreams. Organizational vision must become a true direction for the overall strategy; therefore, it must be equipped with the ability to introduce several quantitative indicators. This study succeeded in explaining how the derivation process should be done. Our proposed model consisting of major steps introduce vision in a more practical basic way, thus providing guidance for a company that wants to be fruitful from their vision. Lastly, the study also provides a guidance related to how vision can be adopted into individual daily performance. By having this mechanism, we believe that vision will no longer mere a dream, but more to a dream that can be achieved.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".