Concepts and Barriers of Female Leadership
Bibliographic record
Abstract
This work aims to gather concepts and barriers faced by women in leadership. Using a qualitative approach, the literature review was conducted following the guidelines established by Galvão and Ricarte (2020) and systematized by the Parsifal system (Protocol for a Systematic Literature Review). Databases such as SciELO, Google Scholar, Scopus, and the Capes Journals Portal were consulted, in Portuguese, Spanish, and English, with publications up to 15 years old, using keywords "leadership", "female", "gender", and synonyms, combined with Boolean operators. The 33 selected articles examined leadership with a focus on female leadership in various contexts, such as politics, education, arts, public service, among others. As a result, it is concluded that promoting more inclusive and egalitarian leadership is essential, as well as conducting additional research to fill an identified gap. This systematic literature review is expected to contribute to the advancement of knowledge on female leadership, providing guidance for future investigations.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".