Rethinking Women in Leadership in the Contemporary Workplace.
Bibliographic record
Abstract
Given the progression of women into labor market, growing concern over improved diversity in the workplace, and legislation advocating for equal opportunities for women and men, it remained a mystery why the opportunity of women into top leadership positions remains limited and why the narrative of ‘think manager think men is becoming a norm. The overarching objective of this paper is to review women in leadership positions in the contemporary workplace. This paper is conceptual in nature and based on extensive review of literature. The paper noted that women experience stumbling blocks that are becoming too tough to break and the obstructions are founded not on lack educational of qualifications, and requisite job experience, but prejudices connected to cultural, societal, organizational, individual, and situational impediments. The paper concludes that women have progressed slightly into leadership roles, but the gap is still much wider. The study recommends expanded opportunity for women into networks and gain sponsorship as a way of climbing into leadership positions. In addition, there is need for inclusive gender policy towards improving women career progression in the workplace. Keywords: Gender, Glass ceiling; Leadership, Career progression, Discrimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".