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Record W2481490427 · doi:10.1057/9781137332813_5

On the Margins: Aboriginal Realities and ‘White Man’s Research’

2015· book-chapter· en· W2481490427 on OpenAlexaboutno aff
Maria I. Medved, Jens Brockmeier

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCensusGeographyPopulationNegotiationArcticWhite (mutation)EthnologyFur tradeSocioeconomicsPolitical scienceHistorySociologyEconomic historyDemographyLawEcology

Abstract

fetched live from OpenAlex

According to the 2011 census, Aboriginal Canadians constitute 4% of the population of Canada (Statistics Canada, 2013). It is a relatively young population, with the average age being 27 years for Aboriginal Canadians as compared to 40 years for non-Aboriginal Canadians. Although ‘Aboriginal’ is the official Canadian term, many indigenous communities prefer terms based on self-governance, of which there are three main groups: the First Nations who are scattered throughout the country, the Inuit who are primarily based in the Arctic and the Métis people, who are of mixed — European (mostly French) and Aboriginal — ancestry. It is widely accepted that one commonality across Aboriginal peoples is their history of colonisation. Part of this history involved the systematic extinction of ‘Indianness’ and policies of ‘aggressive assimilation’. These policies were executed, among others, by ‘negotiating’ treaties that forced indigenous people to live on isolated, often remote, plots of land: the reserves. For most, this way of living brought an end to their traditional nomadic existence. Often reserves were too barren for farming, and if conditions were favourable, farming was not allowed so that Aboriginal people had to buy food and goods from Western settlers. Cultural and spiritual practices such as the potlatch exchange (a ritual involving the gifting of goods to guests) of the Pacific coast communities were also outlawed, but due to the perseverance of many elders some of these ceremonies have managed to survive. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.018
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0470.051
Scholarly communication0.0120.011
Open science0.0030.012
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0100.001

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 head0.065
GPT teacher head0.353
Teacher spread0.288 · 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.

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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooksSame topicIndigenous Health, Education, and RightsFrench-language works237,207