A Practical Process Model to Develop Knowledge Management Life Cycle in National Iranian South Oil Company (NISOC)
Bibliographic record
Abstract
Nowadays, valuable human and knowledge resources will be wasted unless management accepts and supports efforts to gather, sort, transform, record and share knowledge by an optimum implementation process. One of the most problems in some organization is that all people suggestions and comments may not be analyzed carefully. Therefore, it is missed an opportunity about tacit knowledge and experiences that have researchable nature or maybe to convert them to a researchable proposal especially in skill organization layers, whereas they could be developed and advanced by converting to a research to increase the suggestions effectiveness. In order to solving this problem, by a relation between suggestion system and R&T implementation process, all suggestions and comments are analyzed. Then, some of them are selected by R&T expert person or group in the research committee to create a RFP. At last, the R&T department results as explicit knowledge are recorded in the knowledge base and interned in the KM life cycle.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".