The Effectiveness of Feminine and Masculine Leadership Styles in Relation to Contrasting Gender’s Performances
Bibliographic record
Abstract
This paper focuses on the impact and effectiveness of feminine and masculine styles of leadership on contrasting genders in four different economies namely; India, Canada, Pakistan, and United Kingdom's IT Sector. By combining probability and non-probability sampling techniques, the data has been gathered from 248 respondents via semi-structured 5-points scale survey questionnaire. The findings have shown that employees irrespective gender are significant positively affected by feminine leadership style. Additionally, in developing countries; Pakistan and India there is significant use of feminine leadership while developed economies namely; Canada and the UK have higher preference for masculine leadership style. Nevertheless, overall male workers do not have higher preference to work under masculine leadership style while females have higher preference for both masculine and feminine leadership styles. Interestingly, feminine style of leadership is highly demonstrated by males in Pakistan while in other three economies, it is exhibited by female line-managers. Moreover, female line-managers in the UK have shown higher masculine style of leadership in contrast to other economies. There is significant positive relationship between style of leadership and contrasting genders in distinctive economies. Additionally, feminine leadership style is more effective than the masculine leadership style. Feminine leaders demonstrate higher people-orientation and participative style of management whereas masculine leaders rely on task-orientation and autocratic style.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".