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Record W4200484602 · doi:10.1177/25148486211062004

Landscape plasticity and its erasure

2021· article· en· W4200484602 on OpenAlexaff
Janet C. Sturgeon

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRacializationSettlement (finance)State (computer science)GeographyEnvironmentalismSociologyChinaEcologyEthnologyPoliticsPolitical scienceArchaeologyGender studiesLawRace (biology)

Abstract

fetched live from OpenAlex

For centuries, people who call themselves Akha had formed village landscapes of rotating shifting cultivation fields amid regenerating trees together with enduring wooded sites, all under the purview of their ancestors. In Mengsong, an Akha settlement on the ridge separating China and Burma, farmers had managed complex, biodiverse and flexible landscapes for 250 years. In 1996–1997, my extended research there identified cultivation patterns that I called landscape plasticity, referring to farming practices that were highly mutable over space and time, often transgressing state-allocated property lines and the international border with Burma. From 1997 to 2011, a combination of exclusionary state forest policies, the racialization of upland minorities, and a state poverty alleviation project brought landscape plasticity and the ancestors to an end. Using concepts from sentient landscapes, resource access, environmentalism, racialization, and capitalist markets, this paper seeks to explain how landscape plasticity and the ancestors were erased. At the same time, I explore the puzzle of why Akha farmers saw these contingent outcomes as positive

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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 teacher head, 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

Citations3
Published2021
Admission routes1
Has abstractyes

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