Bibliographic record
Abstract
Abstract Positive leadership is a major domain of positive organizational scholarship. The adjective “positive” applies to any leader behavioral pattern (style) that creates the conditions by which organizational members can self-actualize, grow, and flourish at work. Some examples of style are authentic, transformational, servant, ethical, leader–member exchange, identity leadership, and the leader character model. Despite the myriad constructive outcomes that relate to said positive leadership styles, positive leadership it is not without its critics. The three main criticisms are that (a) the field is fragmented and might suffer from conceptual redundancy, (b) extant research focuses on the individual level of analysis and neglects reciprocal and cross-level effects, and (c) positive leadership is naïve and not useful for managing organizations. Our multilevel model of positive leadership in organizations proposes that leaders rely on internalization and integration to incorporate meaningful life experiences and functional social norms into their core self. Further, through self-awareness and introspection, leaders discover and exercise their latent character strengths. In turn, positive leaders influence followers through exemplary role modeling and in turn followers validate leaders by adopting their attributes and self-determined behaviors. At the team level of analysis, positive team leaders elevate workgroups into teams by four mechanisms that shape a shared “sense of we,” and workgroup members legitimize positive leaders by granting them a leader role identity and assuming follower role identities. Finally, at the organizational level, organizational leaders can shape a virtuous culture by anchoring it on universal virtues and through corporate social responsibility actions improve their context. Alternatively, organizations can shape a virtuous culture through organizational learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".