MétaCan
Menu
Back to cohort
Record W2421216786 · doi:10.25071/1920-7336.40385

How KANERE Free Press Resists Biopower

2016· article· en· W2421216786 on OpenAlexvenueno aff
Michele C. Deramo

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsBiopowerRefugeeGender studiesPoliticsNarrativeSociologyState (computer science)Human rightsPolitical scienceLawMedia studies

Abstract

fetched live from OpenAlex

How does a free press resist state biopower? This article studies the development and dissemination of KANERE Free Press, a refugee-run news source operating in the Kakuma Refugee Camp, that was founded to create “a more open society in refugee camps and to develop a platform for fair public debate on refugee affairs” (KANERE Vision Statement). The analysis of KANERE and its impact on the political subjectivity of refugees living in Kakuma is framed by Foucault’s theory of biopower, the state-sanctioned right to “make live or let die” in its management of human populations. The author demonstrates the force relations between KANERE, its host country of Kenya, and the UNHCR through two ongoing stories covered by KANERE: the broad rejection of the MixMe nutritional supplement and the expressed disdain for the camp’s World Refugees Day celebration. Using ethnographic and decolonizing methodologies, the author privileges the voices and perspectives of the KANERE editors and the Kakuma residents they interviewed in order to provide a ground-level view of refugee’s lived experiences in Kakuma. As KANERE records refugees’ experiences of life in the camp, they construct a narrative community that is simultaneously produced by and resistant to the regulations and control of camp administration and state sovereignty. In doing so, KANERE creates a transgressive space that reaches beyond the confines of the camp.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRefuge Canada s Journal on RefugeSame topicGlobal Security and Public HealthFrench-language works237,207