Incremental Transformations: Education for Resiliency in Post-War Sri Lanka
Bibliographic record
Abstract
There is growing evidence to support the relationship between levels of gender inequality in a society and its potential for conflict. Positive attitudes to gender equality in and through education strengthen social cohesion; consequently, there is a need for gender-transformative education for peacebuilding. Drawing on the 4Rs (representation, redistribution, recognition, and reconciliation) framework in conjunction with the idea of incremental transformation with a focus on resilience, this study examines how eleven ethnic minority high school girls from Sri Lanka understand the transformative role of education in their lives as it relates to peace and gender equality. Education was a source of hope for the participants of this study and thus contributed to their resilience. However, rather than fostering and capitalizing on this resilience to build social cohesion and peace, education and the school systems are silencing them. This silencing is evident in the acceptance and normalization of militarization in their communities, daily experiences of gender-based violence (GBV), and the message, through the formal and informal curriculum, that gender equality has been achieved in Sri Lanka.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".