Bibliographic record
Abstract
Women and men are traditionally cast in different roles, with males being leaders in the workplace, home and government. In contrast, communities promote women as caregivers who support male leaders and shape future generations as mothers, mentors and teachers. In recognition of this societal view of women that often led to inequality and inequity, the UNDP listed Gender Equality and empowering women as one of eight Millennium Development Goals. The post-2015 Sustainable Development Goals, adopted by the United Nations in autumn 2015, also included gender equality and empowering women as Goal 5.In its work in the Caribbean, the Caribbean Local Economic Development Project (CARILED) examined gender as it relates to micro, small and medium enterprise (MSME) development in six Caribbean countries. The findings of this study showed gender gaps for both male and female entrepreneurs in different areas of development. Traditionally gendered roles for MSME sectors, access to financing and lack of adequate guidance or community support were some areas that affected men and women differently in the region. The study outlines ways in which male and female leaders can address traditional gender roles by identifying priority areas for development, creating an enabling environment for start-ups and expansion, and fostering a policy and legislative base that facilitates ease of doing business. The recommendations further describe the public–private partnerships needed to successfully meet gender gaps, and the importance of both elected officials and technocrats in inter alia community engagement and advocacy towards local economic development.The importance of gender equality among elected officials and technocrats, and the influence gender has on determining priority areas of focus within local government strategic plans for communities are also set out within this paper.
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 | 0.002 | 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 | 0.016 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".