MétaCan
Menu
Back to cohort
Record W4389726475 · doi:10.7202/1108118ar

“Orgy of Blood” vs “Getting Food on the Table”

2023· article· en· W4389726475 on OpenAlexaffvenue
Roshni Caputo-Nimbark

Bibliographic record

VenueEthnologies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWhalingNationalismRhetoricSpectacleAnachronismModernityReputationEthnographyEpistemeSociologyPoliticsWhaleMedia studiesHistoryAnthropologySocial sciencePolitical scienceArchaeologyLawPhilosophyFishery

Abstract

fetched live from OpenAlex

The Faroese grindadráp is a centuries-old, dramatic spectacle in which an entire pod of pilot whales is slaughtered en masse in a blood-soaked harbour, followed by the distribution of whale meat and blubber to participating villagers. For many Faroese, grindadráp is an embodiment of nationalism, achieved through the primordialization of tradition and the securitization of a beloved food source. For many outsiders, particularly since aggressive anti-whaling campaigns have besmirched the Faroese reputation in the international gaze, grindadráp amounts to a barbarous anachronism intolerable in modern society. This study takes a multivocal digital ethnographic approach to explore how politics, economics, and ethics of grindadráp are understood through social media debates, institutional rhetoric, and an interview. It considers how essentializing discourses of tradition and modernity are framed, their implications for collective action, and some potentialities that are revealed through a shift in perspective from barbaric ritual to dynamic economic practice.

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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.019
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.052
GPT teacher head0.301
Teacher spread0.249 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueEthnologiesSame topicIsland Studies and Pacific AffairsFrench-language works237,207