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Record W1543615953 · doi:10.1111/gec3.12196

Geographies of Human Rights: Mapping Responsibility

2015· article· en· W1543615953 on OpenAlexaff
Nicole Laliberté

Bibliographic record

VenueGeography Compass · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHuman rightsNegotiationPoliticsScholarshipSociologyPolitical scienceEnvironmental ethicsLaw and economicsLawSocial science

Abstract

fetched live from OpenAlex

Abstract Geography has much to contribute to the critical study of human rights. To date, geographic scholarship has tended to follow international trends in connecting the rights discourse to social and environmental justice agendas. Human rights, in these contexts, are assumed to be part of emancipatory politics. It is in challenging this assumption that I suggest critical geographic inquiry has more to offer. In this paper, I review critical geographic literature that explicitly focuses on the spatial processes of human rights around the world. Through this review, I identify a gap in the literature regarding the negotiations of responsibility that shape human rights discourses and practices. I draw on feminist and postcolonial theories of responsibility to highlight geography's potential for examining such negotiations to illuminate how responsibility is claimed, denied, ascribed, enacted, and avoided. Ultimately, I suggest mapping negotiations of responsibility as they cross scales, traverse space, and shape place will provide us with new and evocative lenses for critiquing the place of human rights within an emancipatory politics.

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)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.007
Science and technology studies0.0050.051
Scholarly communication0.0130.020
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.041
GPT teacher head0.317
Teacher spread0.276 · 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 designTheoretical or conceptual
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

Citations26
Published2015
Admission routes1
Has abstractyes

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