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Record W2077190798 · doi:10.1080/14649360701856136

Landscape, mobility, practice

2008· article· en· W2077190798 on OpenAlexfundno aff
Peter Merriman, George Revill, Tim Cresswell, Hayden Lorimer, David Matless, Gillian Rose, John Wylie

Bibliographic record

VenueSocial & Cultural Geography · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
FundersUniversity of British ColumbiaUniversity of NottinghamRoyal Geographical SocietyAberystwyth University
KeywordsPoliticsSociologyHumanitiesRelation (database)Space (punctuation)EthnologyPolitical scienceArtLawPhilosophy

Abstract

fetched live from OpenAlex

This paper is an edited transcript of a panel discussion on ‘Landscape, Mobility and Practice’ which was held at the Royal Geographical Society (with the Institute of British Geographers) Annual Conference in September 2006. In the paper the panel engage with the work of geographers and others who have been drawing upon theories of practice to explore issues of mobility and how we encounter, apprehend, inhabit and move through landscapes. The contributors discuss the usefulness of conceptions of landscape vis-à-vis place and space, and different traditions of apprehending, practising and articulating the more-than-representational dimensions of landscapes. The panel discuss the entwining of issues of power and politics with different representations, practices and understandings of landscape/landscaping, and a number of the panellists position their thinking on the politics of landscape in relation to recent work on the politics of affect.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.033
Scholarly communication0.0080.010
Open science0.0010.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.038
GPT teacher head0.356
Teacher spread0.317 · 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

Citations130
Published2008
Admission routes1
Has abstractyes

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