Understanding Pakistan in Prime Minister Modi’s speeches
Bibliographic record
Abstract
In the road down from democracy to authoritarian rule, the role of perceived ‘enemies’ are often very important. While the existence of these ‘enemies’ might be questionable, they are shown by the beneficiaries of authoritarian rule as the harbingers of evil, and the reasons for whom/which democratic rights must go down the drain. In the context of India, which is still a democracy as I write, there are several candidates who can take up the role of this ‘enemy’ in less fortunate times. A very important one among them is the neighboring country Pakistan. It receives an enormous amount of space in political discourses including Prime Minister Narendra Modi’s speeches, and it is important to scrutinize the importance it receives. The paper takes into account these speeches delivered by the prime minister which mention Pakistan, and uses the method of discourse analysis to inspect parts where Pakistan has been mentioned. Using the same, it tries to understand why the neighboring country has been mentioned, the purpose it serves and how the speaker wants his audience to view Pakistan. It asks if Pakistan is being used as a red-herring to distract attention from more important issues. It assesses the techniques used by the speaker to create the enemy named Pakistan in his audience’s minds.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".