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Record W3007973346 · doi:10.31703/grr.2016(i-i).02

Conflict Resolution: Editorialization of Government- Tehreek-i-Taliban Pakistan Dialogue

2016· article· en· W3007973346 on OpenAlexaff
Hassan Shehzad, Zubair Shafi Ghauri

Bibliographic record

VenueGlobal Regional Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsWorld Federation of Science Journalists
Fundersnot available
KeywordsNewspaperGovernment (linguistics)TerrorismPolitical scienceState (computer science)Conflict resolutionPublic opinionMedia studiesLawPublic administrationSociologyPolitics

Abstract

fetched live from OpenAlex

Every newspaper publishes an editorial every day to state their official opinion on the most important of issues. Among public and official policymakers, editorials are taken seriously. This study undertook Pakistan’s two leading newspapers’ editorials – Dawn and The Nation - on the peace talks between the Pakistan government and the Tehreek-i-Taliban Pakistan (TTP). The editorials published between January 2014 and July 2014 on the dialogues were studied. Using agenda-setting approach, this study found that Dawn published 67 and The Nation 61 editorials discussing stakeholders’ stance on the dialogue, dialogues bodies, and disruption of dialogues to terrorism and TTP terms. The study measured the editorials to answer research questions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.486

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 communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.351
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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