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Record W4413095371 · doi:10.11647/obp.0477.01

Introduction

2025· book-chapter· en· W4413095371 on OpenAlexaff
Mukesh Eswaran

Bibliographic record

VenueOpen Book Publishers · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousMainstreamPovertyConsumption (sociology)Psychological resilienceFourth WorldPolitical scienceSociologyEnvironmental ethicsGeographyDevelopment economicsPositive economicsSocial scienceLawEconomicsSocial psychologyPsychology

Abstract

fetched live from OpenAlex

This introductory chapter first lays out the questions that the book seeks to address. What is responsible for the appalling conditions of North American Indigenous Peoples, where they are in abject poverty and die at excessively high rates due to alcoholism, drug consumption, and suicide? How and why have the particular historical events of the past few centuries led to the current predicament? How can economics contribute to an understanding of the grave conditions of North American Indigenous Peoples? Can we substantiate the analysis of Indigenous communities with rigorous theorizing in a manner that is consistent with mainstream economics? What policy measures would economic analysis suggest in order to ameliorate the serious problems leading to Indigenous “Deaths of Despair”? The point of departure of this book is the role of the erosion of Indigenous cultures. The same approach also sheds light on how many Indigenous communities have exhibited resilience or survivance. The tendency in economics has been to apply routine off-the-shelf frameworks based on assumptions that are not relevant to Indigenous communities. The chapter explains that this book intends to begin remedying that by using standard mainstream economics but with assumptions that are based on the lived experience of Indigenous Peoples. As a result, the theory offered provides explanations for many empirical findings regarding Indigenous Peoples of North America. Apart from its explanatory power, this work suggests some policy measures that are grounded in relevant economic theory and echo the wisdom of Indigenous elders and scholars.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.355
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.001

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 head0.029
GPT teacher head0.287
Teacher spread0.258 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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