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Record W3171039054 · doi:10.24908/b0a67ddbac0f

Economic Aspects of the Indigenous Experience in Canada

2021· book· en· W3171039054 on OpenAlexaboutno aff
Anya Hageman, Pauline Galoustian

Bibliographic record

VenueQueen's University eBooks · 2021
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousConversationPolitical scienceDiscretionGeographySociologyLaw

Abstract

fetched live from OpenAlex

This text explores the economic history and economic potential of Indigenous peoples in Canada. It discusses which institutional arrangements hold them back economical and which institutions assist them going forward, and considers which norms do Indigenous communities hold that inform their priorities and economic behaviour. <> Chapters 1 and 2 introduce the Indigenous Peoples of Canada – First Nations, Métis and Inuit – and their current demographic and income statistics. Chapters 3-12 describe their cultures, economies and geopolitics up until the late twentieth century. Chapters 13 and 14 discuss how discrimination against minorities can be modeled and measured. Finally, Chapters 15+ describe present-day issues in the economic development of Indigenous communities. <> Note for Instructors: Instructors may wish to begin the term of study with presentations or readings on the peoples indigenous to the school’s location. As the course progresses, instructors can lead students to discover how the topics covered in the book apply to local communities past and present. Instructors can also make students aware of local opportunities for Indigenous – non-Indigenous interaction and cooperation. This text flows in chronological order until Chapter 12. Instructors should use their own discretion about whether and when they want to use Chapters 12-14. Chapter 15 picks up the historical thread. The use of talking circles and other discussion forums is recommended, as conversation is a traditional Indigenous teaching method, and the issues covered in this book are emotionally weighty.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.893
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.171
Teacher spread0.164 · 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
GenreOther

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

Citations17
Published2021
Admission routes1
Has abstractyes

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Same venueQueen's University eBooksSame topicCanadian Identity and HistoryFrench-language works237,207