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The Atta Abad Landslide and Everyday Mobility in Gojal, Northern Pakistan

2013· article· en· W2072873909 on OpenAlexafffund
Nancy Cook, David Butz

Bibliographic record

VenueMountain Research and Development · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLandslideGeographyEthnographyMetropolitan areaNatural disasterNatural (archaeology)ArchaeologyGeologyMeteorology

Abstract

fetched live from OpenAlex

In early 2010, the massive Atta Abad landslide blocked the Hunza River in the Gojal region of northern Pakistan. It also buried or flooded 25 km of the Karakoram Highway, the only vehicular transportation route connecting this region to the rest of Pakistan. Since the Karakoram Highway opened in 1978, road mobility has become deeply integrated into the everyday economies and time–space fabric of Gojali households. In this paper, we focus on what happens when a natural disaster unexpectedly slams the brakes on movement as a way to understand more fully the sociodevelopmental implications of roads in the rural global South. We review the history of mobility in the region to explain the importance of the Karakoram Highway as a mobility platform that restructured sociospatial relations in Gojal. We then turn to interviews, ethnographic fieldwork, and local news sources to outline how residents of 4 Gojali communities were experiencing the economic, social, and emotional impacts of landslide-induced mobility disruptions in the 18 months following the disaster.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.006
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.387
Teacher spread0.321 · 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 designObservational
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

Citations30
Published2013
Admission routes2
Has abstractyes

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