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Record W4415350437 · doi:10.1080/00220388.2025.2569393

Keeping the Enemies Closer? Exporting Behaviour of Firms Under Conflict

2025· article· en· W4415350437 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

VenueThe Journal of Development Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Social Dynamics
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsProduction (economics)Government (linguistics)Context (archaeology)Work (physics)

Abstract

fetched live from OpenAlex

This paper uses the terrorist attack in India in 2016 as a quasi-natural experiment to investigate the effect of terrorist activities on exporting behaviour of firms. Using transaction-level international trade data for the universe of exporting firms in Pakistan, we employ a difference-in-differences identification strategy to show that exporters experience a smaller exports value, quantity, and unit value growth in the Indian market after the attack, relative to other countries. Our results shed light on both the intensive and extensive margins of trade, and document heterogeneous responses to the shock across firms, products, and shipping locations. Smaller exporters experienced a larger drop in exports volume and price, while more import-intensive firms, particularly those importing from India, did not witness a decrease in demand. Similarly, the study detects asymmetric responses across products and shipping ports based on proximity to the location of the attack.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.060
GPT teacher head0.382
Teacher spread0.322 · 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