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Record W4392885402 · doi:10.1111/area.12933

Caring for the river‐border: Struggles and opportunities along the Salween River‐border

2024· article· en· W4392885402 on OpenAlexaff
Vanessa Lamb

Bibliographic record

VenueArea · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsYork University
FundersUniversity of Melbourne
KeywordsIrregular migrationEconomic geographyPolitical scienceRegional scienceSociologyGeography

Abstract

fetched live from OpenAlex

Abstract Geographers have shown how borders rely on the enactment of state power and violence to reinforce territorial integrity and sovereign authority, or even perpetuate the destruction of nature. Moving away from an emphasis on violence, in this paper, I take an approach to borders and bordering that emphasises the opportunities of the border when it is also a river to understand borders as a resource and site of engagement with the state by a range of actors, including variants of care. To illustrate this, I draw on longstanding research along the Salween River, the 120 km stretch where the river forms the Thai–Myanmar (Burma) border, to reveal the ways in which borders as rivers can provide new insights into socio‐natural bordering processes. In particular, I illustrate a range of ways local residents are caring for a river‐border, and how even an ‘exploding’ or ‘hungry’ river‐border can be a fragile space for care and for non‐state actors to enact the border ‘differently’ in everyday life.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.019
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.018
Scholarly communication0.0070.005
Open science0.0010.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.054
GPT teacher head0.332
Teacher spread0.278 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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