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Record W2626007162 · doi:10.6082/2vces-3y921

Language and Territorialization

2017· article· en· W2626007162 on OpenAlexaboutno aff
Donna Patrick, Gabriele Budach

Bibliographic record

VenueOpen MIND · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFoodwaysSeal (emblem)PoliticsFood studiesSovereigntyPower (physics)SociologyConsumption (sociology)Political scienceLawHistoryAnthropologySocial scienceArchaeology

Abstract

fetched live from OpenAlex

In this paper we analyze two March 2010 events in Ottawa, Canada involving the preparation and consumption of seal-meat: one an Inuit seal feast, held at a Inuit community center, in which raw seal was carved and eaten in accordance with traditional Inuit practices; the other a "seal lunch", held in the Parliamentary Dining Room for Members of Parliament, in support of the Canadian seal-hunt. Methodologically, we make use of both participatory action research and detailed textual analysis of media reports, and frame our analysis in terms of moral geographies, social and cultural values associated with food, and meaning-making systems embedded in discourses, which serve to construct and constitute particular power relations. Doing so leads us to claim that the two seal-meal events drew on and conveyed radically different meanings. The Inuit meal, though not overtly political, represented an act of food sovereignty and a claim to Inuit territoriality in the city. The Parliamentary seal lunch, by contrast, had a clear political aim, as a form of protest against the European Union decision to ban seal meat and other products. Yet, while purporting to support Inuit seal-hunting, the Parliamentary meal effectively communicated the utter foreignness of seal meat and Inuit foodways with respect to Western tastes and discourses about food and environmentalism—a fact that emerges through our ethnographic and media analysis of the two seal lunch events.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.999

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 studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.085
GPT teacher head0.470
Teacher spread0.385 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicIndigenous Studies and EcologyFrench-language works237,207