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Record W2032323033 · doi:10.1068/a44352

Field Expertise in Rural Land Management

2012· article· en· W2032323033 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsAgriculture Food and Rural Development
FundersEconomic and Social Research Council
KeywordsStewardship (theology)NegotiationUnderpinningField (mathematics)Context (archaeology)Work (physics)Land managementNatural (archaeology)Land useSociologyEnvironmental planningEnvironmental resource managementPublic relationsPolitical scienceEngineeringGeographySocial scienceArchaeologyEconomicsCivil engineeringPoliticsLaw

Abstract

fetched live from OpenAlex

This paper explores the expertise of field-level advisors in rural land management. The context is the English uplands and negotiation over a Higher Level Stewardship agreement. An observed encounter between a hill farmer, his retained land agent, and an ecologist working for Natural England illustrates the multiple roles that field-level advisors have in regulating, directing, and influencing contemporary land management. The paper draws on field notes taken during work shadowing and in-depth interviews, to reflect upon the relationships that constitute field expertise—not only between farmer and advisor, but amongst the advisors too (and those who advise them). We argue that expert—expert interaction and the emergence of networks of practice are crucial to the development of field expertise and are key factors in the increasing complexity of the decision making underpinning contemporary land management.

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.

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 score0.043
Threshold uncertainty score0.139

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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