Exploring Intersectionality: Theoretical Concept and Potential Methodological Efficacy in the Context of Nepal
Bibliographic record
Abstract
This article engages in theoretical discussions of intersectionality on such issues as: how does Kimberle Crenshaw's intersectionality theory function in various forms of social divisions, and how do various scholars respond to it? Why is intersectionality theoretically and methodologically critical to examining Nepali political and social contexts, especially on women and Dalit's issues? This article examines the overview of intersectional theoretical standpoints explicitly based on Crenshaw's ideas and how it problematizes political practices of domination and discrimination against minority groups in societies today. Rather than providing an empirical and positivist approach to findings, this write-up offers a theoretical framework that helps conceptualize and utilize it in examining power exercise and politics in the Nepali context. It emphasizes discourse analysis to explore the systemic discrimination and the genealogy of structural violence to moot debates about central and marginal subjects concerning women and Dalit issues in Nepal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".