Bibliographic record
Abstract
The current literature on value congruence between followers and ethical leaders has paid little attention to congruence of explicit ethical values, and even when ethical values are included in congruency, they are not well conceptualized.As ethical conduct is increasingly becoming important in today's organization, ethical values that followers share with leaders is vitally important for leadership in the workplace.In addressing this issue, this study aims to identify the core explicit ethical values followers rely on to answer the question of value congruence between senior leaders and followers and how followers understand these ethical values.Findings from in-depth interviews with 15 participants in both private and public sector organizations suggest a lack of precise knowledge of what counts as ethical value.Results indicate ethical values associated with lower-level leaders are also applicable to senior leaders, however, loyalty, humility, sincerity, and trust are distinctively important ethical values associated with senior leaders.The concepts of humility and trust are not clear.There are values which are elements of general leadership behavior but are not necessarily ethical.Findings suggest a lack of precise knowledge in identifying and explaining explicit ethical values.Knowledge of ethical values will have an impact on the effects of ethical value congruence between senior leaders and followers.
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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 | 0.006 | 0.027 |
| 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 | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".