RELATIONSHIP AUTHENTIC LEADERSHIP TO COACHING EFFICACY ANALYSIS
Bibliographic record
Abstract
Increasing business conditions in the second quarter of 2017 and the number of businesses in Indonesia that increase each year, make the competition tighter and increase the number of competitors. In order to compete and survive in the fierce competition, an effort is needed that can help solve the problem. One of the solutions is to find people who can help in deal with problems and exchange ideas to grow the business. In this context, a business coach is the person. Business coaches will help to develop selfpotential and become coach as well as companion in exchange of knowledge and information. Thus, will open his mind and be able to issue ideas that can grow his business. However, there are still many who do not believe in the role of a coach who can help to develop self potential. Thus, the trust between the coach and his client must be built first. The trust between these coach-clients can be obtained from authentic leadership perceived or owned by a coach. This study aims to determine the relationship of authentic leadership to coaching efficacy. The population in this study is business coaches in Indonesia and the sample is 100 business coaches. The research method used is quantitative method. Study results indicate authentic leadership has a positive relationship to coaching efficacy. Business coaches are expected to improve self-awareness and technique during business coaching sessions.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".