Intrinsic leadership: Scale development and testing its validity
Bibliographic record
Abstract
Intrinsic leadership is a new concept of leadership and is built based on philosophically and psychologically of the intrinsic meaning. The basic philosophy of its development is a holistic leadership philosophy that integrates cognitive, affective, conative, and spiritual as the core of leadership. The purpose of developing intrinsic leadership is to create a vision and value congruence, empower cooperation between all stakeholders and especially build an organization like a family so that it creates attachment, trust, a motivation that has an impact on organizational performance. Intrinsic leadership is conceptually different from the previous concept of leadership that only rests on one or several elements that the leader has as a human. Development of measurement scales and validity testing provides a foundation that facilitates quantitative research in intrinsic leadership. There are three studies carried out, the first is scale development and testing the content validity of intrinsic leadership measurements. Testing the content validity is accomplished by analyzing and calculating the content validity index (CVI) and Kappa coefficient data are obtained from a panel expert consisting of eight experts. The second study is related to testing construct validity of intrinsic leadership using EFA from 186 data collected from owner-managers of SMEs. The results show that there are twenty-six indicators extracted into eight factors that are intrinsic leadership measurement scale. The third study aims to test empirically or confirm measurement model of intrinsic-leadership using CFA.
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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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".