Portraying the “Other” in Textbooks and Movies: The Mental Borders and Their Implications for India–Pakistan Relations
Bibliographic record
Abstract
Borders have been traditionally known just as physical cartographic boundaries on maps. However, the epistemological and ontological underpinnings of Border Studies have witnessed constant evolution in the past century. This has brought to the fore the importance of mental borders along with the physical borders. When it comes to a region like South Asia, the lack of regional integration is conspicuous. One of the reasons for this is the existence of mental borders along with rigid physical borders. The paper seeks to understand the process of creation of mental borders between the two South Asian neighbours by probing it from the point of view of school textbooks and cinematic narrative. School textbooks are the most fundamental building blocks of knowledge in any society. Analysis of these texts brings forward the metaphysical construction of mental borders at a very early stage. Subsequently, cinema as a mode of popular culture is an effective tool in order to understand social phenomena from people’s perspective. Here, the process of meaning creation is largely embedded in linguistics and is derived from people’s experiences. The deconstruction of these data sources leads to the understanding of the process of mental border formation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".