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Record W2017253567 · doi:10.1080/13613320220139617

Aboriginal and Indigenous People's Resistance, the Internet, and Education

2002· article· en· W2017253567 on OpenAlexfundno aff
Judy M. Iseke-Barnes

Bibliographic record

VenueRace Ethnicity and Education · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCyberspaceThe InternetResistance (ecology)IndigenousSociologyColonialismContext (archaeology)NarrativeMedia studiesGender studiesPublic relationsSocial sciencePolitical scienceLawHistory

Abstract

fetched live from OpenAlex

This article examines exchanges in an Internet newsgroup which is focused on issues pertaining to Aboriginal peoples. The examination of these exchanges highlights cyberspace as sites where colonial misunderstandings are evident and resistance to these dominant discourses is possible. Issues of pedagogy and Aboriginal peoples on the Internet are explored. Given that home and school use of the Internet is ever increasing, it is of growing importance for educators and academics to consider ways that cultural groups are represented in this context. Internet texts, just as texts, books, and media before them, produce cultural narratives in regard to Aboriginal peoples. How are cultures represented? Who controls these representations? This article provides examples of resistance to colonial discourses about Aboriginal peoples but cautions that there are risks with the increasing commercialisation of the Internet that dominant discourses might prevail.

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)0.003
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.015
Scholarly communication0.0090.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.018
GPT teacher head0.337
Teacher spread0.318 · 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

Citations28
Published2002
Admission routes1
Has abstractyes

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