Prevalent Leadership Profile: A Research on Management of Innovation in the Military Organization
Bibliographic record
Abstract
In this work we investigate prevailing leadership in a military organization.Our study has a multi-disciplinarily nature as it focuses on Organizational Innovation and the profile of a leader as well as a little investigation on history of the development of a military organization Our main objective is to identify the profile of a strategic prevalent leadership at the Firemen Corporation in Porto Velho, Rondonia -Northern Brazil.We propose to describe the process of nomination for someone to exercise the role of a leader used in the military structure.In this context, we make a survey of the prevalent elements considering the profile of leadership in the face of organizational innovation having in mind the expectation of the people involved in the process of research.At the end we conduct a critical analysis comparing the prevalent elements identified in our study with the expectation of innovation revealed by the military structure investigated.We apply the method of content analysis through convenient procedures.As a result, we present the elements which show prevalence for the definition of the leadership profile of the investigated military structure.We hope our research can contribute to the studies in the field of leadership, as we still have few references in the area of knowledge.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".