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Record W4307009067 · doi:10.1177/00207020221135299

False negotiations and the fall of Afghanistan to the Taliban

2022· article· en· W4307009067 on OpenAlexaff
S. Yaqub Ibrahimi

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsCarleton University
Fundersnot available
KeywordsAfghanNegotiationPolitical scienceGovernment (linguistics)Political economyPoliticsSettlement (finance)PeacebuildingDevelopment economicsLawSociologyEconomics

Abstract

fetched live from OpenAlex

This paper examines the dark side of Afghanistan’s peace negotiations that started under Donald Trump’s South Asia policy in Doha, Qatar, in September 2018 and ended, without a meaningful success, in December 2020. The paper examines both the United States–Taliban and the intra-Afghan negotiations and discusses the impact of the false negotiation behaviour by the Taliban and the Afghan government on the peace talks. It also explains how, in the absence of conditions for a political settlement, parties to the conflict acted as false negotiators, which resulted in the failure of efforts to launch a genuine peace process and in the Taliban’s military domination of Afghanistan.

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)0.005
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.023
Scholarly communication0.0110.005
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.304
Teacher spread0.296 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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