Competency Experience-Based Training (CEBT) Model with Ubiquitous Community of Practice (U-CoP) to Enhance Transformation Digital Supervisor
Bibliographic record
Abstract
Research subject Competency Experience-Based Training (CEBT) Model with Ubiquitous Community of Practice (U-CoP) to Enhance Transformation Digital Supervisor. This research aims to evaluate the digital supervisor competency trained with the Competency Experience-Based Training (CEBT) Model with Ubiquitous Community of Practice (U-CoP). The researcher has divided the research process into 3 steps as follows: Step 1: To develop the Competency Experience-Based Training (CEBT) Model with Ubiquitous Community of Practice (U-CoP) to enhance transformation digital supervisor. Step 2: To develop the Competency Experience-Based Training course with Ubiquitous Community of Practice (U-CoP) to enhance transformation digital supervisor. Step 3: Evaluate the digital supervisor competency trained with the Competency Experience-Based Training (CEBT) Model with Ubiquitous Community of Practice (U-CoP). The results of the research were as follows: 1) the Competency Experience-Based Training course with Ubiquitous Community of Practice (U-CoP) to enhance transformation digital supervisor, it consists of 3 main processes and 10 sub-steps. Ubiquitous community of practice consists of 2 parts. 1) Community of practice, and Ubiquitous technology 2) Competency Experience-Based Training course with Ubiquitous Community of Practice (U-CoP) to enhance transformation digital supervisor consisting of 6 components. The results of the evaluation of digital supervisor competency in training participants with a model developed using pre-training and post-training surveys showed that trainees scored higher than their pre-training digital supervisor competency at a statistically significant .01 level.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".