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Record W4289437234 · doi:10.1515/9781474420969

Rural Modernity in Britain

2018· book· en· W4289437234 on OpenAlexaboutno aff
Kristin Bluemel, Michael McCluskey

Bibliographic record

VenueEdinburgh University Press eBooks · 2018
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsModernityGeographySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Defines the interdisciplinary field of Rural Modernity through analysis of British literature, art and cultureRural Modernity in Britain argues that the rural areas of Britain were impacted by modernisation just as much - if not more - than urban and suburban areas. It is the first study of modernity and modernism to focus on rural people and places that experienced economic depression, the expansion of transportation and communication networks, the roll out of electricity, the loss of land, and the erosion of local identities. Who celebrated these changes? Who resisted them? Who documented them?Essays in this collection make the case that the rural means more than just the often-studied countryside of southern England, a retreat from the consequences of modernity; rather, the rural emerges as a source for new versions of the modern, with an active role in the formation and development of British experiences and representations of modernity.Key FeaturesIntrodues readers to concept of rural modernity that locates its critical intervention in fields of modernism and modernity studiesSplit into five sections addressing Networks, Landscapes, Communities, Heritage, and WarIncludes In dialogue with" suggestions to guide readers across interdisciplinary contents of diverse chaptersContributors from England, Scotland, USA, New Zealand and Canada, representing fields of literature, art history, history, geography, and cultural studies

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.641

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 science0.0010.000
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 head0.018
GPT teacher head0.189
Teacher spread0.171 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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