Empowerment of women through an innovative e-mentoring community platform: implications and lessons learned
Bibliographic record
Abstract
This article presents an overview of an e-mentoring community platform that intends to promote women’s empowerment. Women face the so-called glass ceiling effect, the barrier that keeps them from rising to the upper rungs of the corporate ladder, regardless of their qualifications or achievements. We aim to eliminate the stereotypical profile of women as excluded from economic, political, and professional life and promote women’s empowerment, equality, and social coherence. To this aim, we aspire to develop Womenpower, an innovative e-mentoring community platform that intends to link women mentors and mentees in the areas of academia, business, and healthcare. Given the nature of this endeavor, there is a need to approach the development of the e-mentoring platform as a horizontal process and democratize the design, allowing for different perspectives of stakeholders to be heard and determine the design decisions. This article delineates the approach adopted for democratizing the design process and maximizing intended users’ involvement in the development process. Finally, we conclude with implications for researchers and practitioners in Community Informatics and recommendations for promoting the participation of women in the fields of academia, business, and healthcare.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".