Scaling Gender Equality and Social Inclusion in Schools Through Stakeholder Mapping: Teachers’ Perspectives on Enablers and Barriers
Bibliographic record
Abstract
Stakeloder engagement is crucial for driving Gender equality and social inclusion (GESI) initiatives. Using Participatory Action Research, this situational analysis (SAS) study explored people, place, and things catergorised into four scaling groups: initiators, enablers, competitors, and the impacted for scaling GESI initiatives in schools and communities effectively through stakeholder engagement. The research during the participation axis phase administered a survey questionnaire to teachers, including five males and females, to analyse stakeholders responsible for promoting GESI in schools. Key findings revealed that Initiators, such as school leaders and educators, played a crucial role in driving GESI-related changes within schools. Enablers, including local community leaders and supportive teachers, were instrumental in ensuring the successful implementation and sustainability of GESI initiatives. However, challenges arose from Competitors, including senior citizens, religious practitioners, and some local leaders, whose conservative beliefs often impeded progress. The Impacted group, consisting of marginalized students, teachers, and women affected by gender inequality, was identified as the primary beneficiary of successful GESI initiatives. Despite its valuable insights, the study has a few limitations. The perspectives gathered were primarily from teachers, which may not fully capture the views of all relevant stakeholders, particularly those from marginalized groups or other community members. Additionally, the categorization of stakeholders, especially within the Competitors and Impacted groups, may lack sufficient nuance in certain contexts. The study recommends a more inclusive stakeholder engagement process that involves all groups, including Competitors, through participatory dialogue to address resistance and foster collaborative solutions. Continuous capacity-building efforts for teachers, school leaders, and policymakers are essential to ensure the sustainability of GESI initiatives. Future research should expand the analysis to include a broader range of stakeholders and account for regional variations in the implementation of GESI initiatives.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".