Bibliographic record
Abstract
In my Major Research Project, I explore how the India-Pakistan partition of 1947 is conceptualized in a popular media text. Specifically, I look at a TV series produced in Pakistan that explores the partition and the events immediately preceding it, that led to the splitting of India into India and Pakistan from a nationalistic perspective. Major themes that are noteworthy of analysis include gender relations, notions of belonging and community, nationalism and identity, contextualization and impact of media, and trauma. Moreover, I pay attention to how gender relations and notions of family are conceptualized in relation to nationalistic ideologies, and how both are impacted during traumatic events. In particular, my research interest includes studying how this media depiction of the partition plays into or contests dominant narratives of the nation and citizenship along the lines of religious and gender classifications. The literature review below aims to explore theoretical conceptualizations of my areas of interest in order to enable my media text analysis to be situated in relation to existing literature.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".