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Record W4387616649 · doi:10.1080/01426397.2023.2266394

Landscapes of care: politics, practices, and possibilities

2023· article· en· W4387616649 on OpenAlexaff
Sara Jacobs, Taryn Wiens

Bibliographic record

VenueLandscape Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPaternalismPoliticsSituatedPower (physics)SociologyEnvironmental ethicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Care offers a framework to shift relations with land and suggest alternative possibilities to dominant, and often extractive, landscape practices.With increasing attention to inequities in labour, histories of erasure and exclusion, ongoing harms of colonisation, and the uneven impacts of how climate change reshapes landscapes, care has come to describe hopeful ideas for how landscapes are researched, maintained, and designed.Yet, care is not a simple solution to complex problems.In reviewing landscapes of care through politics, practices, and possibilities, we assert that care situated in landscape must acknowledge the relationship between the control of land and power, and resist paternalistic modes of care which normalise social and environmental injustices manifest in landscapes.As landscape scholars increasingly engage topics of care, we urge for a more critical politics of care that is reflective of how landscape relations generated through care reveal and remake relationships to power.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.080
Scholarly communication0.0140.011
Open science0.0010.009
Research integrity0.0030.004
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 head0.111
GPT teacher head0.467
Teacher spread0.355 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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