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Understanding Pakistan in Prime Minister Modi’s speeches

2020· article· en· W4383031460 on OpenAlexaff
Sohom Roy, Shabdita Tiwari, Dhruv Kaushik, Sumit Randhir Singh

Bibliographic record

VenueInternational Journal of Political Science and Governance · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsPrime ministerDemocracyAdversaryAuthoritarianismContext (archaeology)PoliticsPolitical sciencePrime (order theory)Media studiesLawPolitical economySociologyHistoryComputer securityComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.118
GPT teacher head0.369
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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