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Record W2977383234

The role of protests as platforms for action on sustainability in the Kullu Valley, India

2010· article· en· W2977383234 on OpenAlexfundno aff
Vanessa Lozecznik

Bibliographic record

VenueMspace (University of Manitoba) · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAction (physics)SustainabilityPolitical scienceBusinessEcology
DOInot available

Abstract

fetched live from OpenAlex

The Himalayan region of India has a surprisingly fragile ecosystem due in part to its geomorphic characteristics. In recent years the Himalayan ecosystem has been disturbed in various ways by both human and natural processes. Large developments threaten ecosystems in the area modifying local land use and subsistence patterns. This has important implications for the sustainable livelihoods of the local communities. People in these areas are very concerned about the lack of inclusion in development decision-making processes and the negative effects of development on their livelihood. Protest actions are spreading throughout Himachal Pradesh, not only to stop developments but also to re-shape how developments are taking place. The village of Jagatsukh was selected for in-depth study. That is where people started to organize around the Allain Duhangan Hydro Project and also where the protest actions in relation to the Hydro Project actually started. The overall purpose of this research was to understand the role of protests as a vehicle for public participation in relation to decisions about resources and the environment and to consider whether such movements are learning platforms for action on sustainability.

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)0.003
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 score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.197
Teacher spread0.187 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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