Removing barriers to recruiting, retaining and advancing women in science and technology fields for promoting green industries in Colombia
Bibliographic record
Abstract
In the last decade, the Gender-Transformative Approach (GTA) has pivoted the frame used by international cooperation and academic research in gender integration; however, there is insufficient information regarding how the GTA operates in executing projects and programs. We evaluated a development project in rural areas of Valle del Cauca (Colombia). The project was implemented by Autónoma University and funded by the International Development Research Center (IDRC). The project sought to remove barriers to recruiting, retaining and advancing women in science and technology fields for promoting green industries in the South American Country. The project was designed within the GTA as one of the theoretical approaches for its implementation. Our goal was to identify to what extent the project's activities have impacted the successful inclusion of GTA. Based on the Typologies of Change: Gender Integration in Agriculture and Food Security Research developed by the Royal Tropical Institute (KIT), we designed a methodology that allows us to map i) what strategies enable gender equality and transformation in the project ii) what level the strategies impact (individual, community, organizational) iii) the relationship between the strategies and the results.Our research included an exploratory phase where we conducted informal interviews in the field. We reviewed the project's documents and conducted 12 semi-structured interviews with diverse actors (beneficiaries, project team, institutions, and organization representatives). Our results indicate that combining soft and hard strategies enables transformations at the individual and community levels.
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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 | 0.004 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".