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Record W2244428322

Rural Communities and Landscape Change: A Case Study of Wild Ennerdale

2008· article· en· W2244428322 on OpenAlexvenueno aff
Ian Convery, Thomas Dutson

Bibliographic record

VenueJournal of rural and community development · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsAlienationCommissionArgument (complex analysis)GeographyNational parkLocal communityPolitical scienceEnvironmental planningEnvironmental resource managementEconomic growthSociologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Ennerdale Valley is located in the Lake District National Park of northwest England. The valley has been managed as coniferous plantation forest since the 1920s by the Forestry Commission (FC). Since 2002, however, the FC has been a partner (along with the National Trust and United Utilities) in the Wild Ennerdale (WE) rewilding initiative, which alongside a more a naturally evolving landscape, also aims to provide socio-economic benefits for the local community. This paper considers the relationship between WE and the cultural landscape of Ennerdale Valley and has identified disparities between the WE view of engagement and participation and corresponding feelings of alienation, dispossession, and dislocation expressed by some members of the local community. The paper presents an argument for stronger links between WE and the Ennerdale community. In particular, there needs to be much greater appreciation of the role the rural community has played, and continue to play, in shaping the landscape of Ennerdale. Recognition of this role is important in terms of delivering a sustainable future both for the valley and for WE.

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)0.002
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 score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.005
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0020.002
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 head0.047
GPT teacher head0.241
Teacher spread0.194 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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