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Record W2288806138 · doi:10.18584/iipj.2016.7.1.1

Media Representations of Policies Concerning Education Access and their Roles in Seven First Nations Students’ Deaths in Northern Ontario

2016· article· en· W2288806138 on OpenAlexaffvenueabout
Kevin Gardam, Audrey R. Giles

Bibliographic record

VenueInternational Indigenous Policy Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThunderGovernment (linguistics)IndigenousState (computer science)Economic growthColonialismPolitical scienceSociologyPublic administrationGeographyLawEconomics

Abstract

fetched live from OpenAlex

We employed postcolonial theory, a case study methodology, and critical discourse analysis to investigate the ways in which non-First Nations and First Nations news sources produced understandings of the role(s) that education policies may have played in the deaths of seven First Nations students in Thunder Bay, Ontario, Canada. We found that national non-First Nations media sources produced the discourse that First Nations peoples require federal government policy as a form of intervention in their lives. Further, we found that though these media sources focused on criticizing the present state of First Nations education, they ignored the colonial processes and policies that contributed to a situation that resulted in the students attending high school in Thunder Bay, rather than their home communities. First Nations and local (Thunder Bay) non-First Nations media sources, however, emphasized the need for greater cooperation between the Canadian government and First Nations peoples to resolve the long-standing policy issues that continue to affect First Nations youth and their education in northern Ontario. These findings point to important differences in the ways in which various forms of media cover First Nations policy issues.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.386
Teacher spread0.356 · 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 designObservational
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

Citations8
Published2016
Admission routes3
Has abstractyes

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