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Record W6980859619

Cwm:The Fair Country

2025· book· en· W6980859619 on OpenAlexaboutno aff

Bibliographic record

VenueUlster University Research Portal (Ulster University) · 2025
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsWelshWork (physics)Quarter (Canadian coin)Closure (psychology)Accidental
DOInot available

Abstract

fetched live from OpenAlex

In the mid 1990s, Ken Grant began to photograph in the South Wales Valleys, as communities came to terms with the closure of mines after strikes and protest a decade before. <br/><br/>To those who know the immersive work he made over decades amongst his contemporaries in Liverpool, Cwm -The Fair Country might seem like a departure. Yet, in parallel, Grant has worked steadily in the Valleys for more than a quarter of a century, to approach similar themes of labour and endurance in a new way. Working amongst communities who have weathered changes in a region once sustained by mining and steel, he weaves scenes of the former industrial terrain and ribboned valley housing through photographs of the many teams of wild horses left to move through it for pasture, in a layered and tender account of a region that would eventually become his home.<br/><br/>A hill is studded in canvas to keep a former coal tip from risk of landslip and tragedy; a playground stands firm as the steel plant that once surrounded it is dismantled. The horses have witnessed what industry did to the Welsh valleys and its people over centuries and stand, with stoic beauty, long after the furnaces and mines have quietened. Ken Grant's photographs stand as a singular account of a region often photographed. In work built over decades, Cwm -The Fair Country foregrounds beauty, scars and the signs of lives that persist despite the weight of an industry's passing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.277
Teacher spread0.254 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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