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Record W4382893817 · doi:10.1177/09763996231155632

Barriers to Indo-Pak Trade: A Case Study of Land Routes

2023· article· en· W4382893817 on OpenAlexaff
Zahid-ul-Islam-Dar, Sandeep Kaur, Vijay Kumar Chattu

Bibliographic record

VenueMillennial Asia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDocumentationBusinessControl (management)International tradeEconomicsManagement

Abstract

fetched live from OpenAlex

Concerning land routes, the study aims to document some crucial barriers, which are relatively easy to address but potent enough to expand trade between India and Pakistan. Using field research, this article examines the factors that impede trade between India and Pakistan through land-border crossings—Attari–Wagah border in Punjab and, Chakkan da Bagh, Poonch, and Salamabad Uri, Baramulla, the two land-border crossings in Jammu and Kashmir. Semi-structured questionnaires are administered to traders and unstructured interviews are held with other stakeholders. The findings such as security issues, inadequate banking facilities, inadequate physical infrastructure, communication lacunae, excessive paperwork, and lack of arrangements for traders’ meets are documented as some of the prominent impeding factors in overland trade between the two nations. The prominent barriers perceived by the traders concerning Attari and Cross-the Line of Control (LoC) trade are excessive documentation, complex procedures, and nonavailability of banking facilities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.320
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2023
Admission routes1
Has abstractyes

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