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IRAN’S AERIAL STRIKES: MOTIVATIONS AND PAKISTAN’S MEASURED RESPONSE

2024· article· en· W4401659982 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMargalla Papers · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsConcordia University
Fundersnot available
KeywordsPolitical scienceGeography

Abstract

fetched live from OpenAlex

the January 16, 2024, missile and drone strike by Iran against alleged sanctuaries of Jaish al-Adl inside Pakistan’s province of Baluchistan provoked a short crisis between Islamabad and Tehran, culminating in retaliation by Pakistan on January 18, 2024. The lack of close coordination on their shared frontier, amidst severe issues of drug trafficking from Afghanistan, terrorism in Baluchistan, and Iranian concerns about infiltration, undermined a mutual understanding between these two countries. Pakistan was entirely surprised, never having been attacked by Iran before. Despite the strike during the election campaign to determine Pakistan’s next governing party and executive, Islamabad resisted retaliation until it failed to elicit a conciliatory explanation from Tehran. Iran’s attack was most likely the result of a hardline foreign policy initiative decided and implemented by the Islamic Revolutionary Guard Corps (IRGC) as part of its escalating conflict with Israel and the defence of the Houthis in Yemen. Following Pakistan’s measured and proportionate response, Tehran and Islamabad, encouraged by China, the US, Russia, and Türkiye, diplomatically defused the tension, and bilateral relations normalised.

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.

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)0.000
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 score0.992
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.311
Teacher spread0.280 · 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