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Record W4392815707 · doi:10.29173/jaed303

Accounting And Aboriginal Peoples: From the Bottom Line to Lines of Relation

2011· article· en· W4392815707 on OpenAlexaboutno aff
Isobel M. Findlay, Warren I. Weir

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsPaternalismIndigenousColonialismCommissionEconomic JusticeTreatyTraditional knowledgePolitical scienceRelation (database)Environmental ethicsSociologyLawEcology

Abstract

fetched live from OpenAlex

According to the Royal Commission on Aboriginal Peoples, rebuilding Aboriginal economies and nations will require "radical departures from business as usual." In the interests of such radical change, this essay traces the role of accounting in obstructing and obscuring Aboriginal peoples' opportunities, achievements, and contributions and explains how Aboriginal thinking and institutions are helping redefine success measures and increase choice by making visible alternative paths and promoting sustainable development for all. Building on ongoing efforts to think and act outside colonial conceptual boxes and celebrate culturally meaningful, holistic Aboriginal economic performance, this essay recommends a double strategy to address the historical impact of traditional accounting on Aboriginal peoples and economies by (a) displacing old paternalistic models that constructed Aboriginal "problems" and (b) respecting and learning from Aboriginal powers, achievement, and measures of success. Only when Indigenous knowledge and values are put at the centre of authoritative practices will accounting do justice to the specificities of Aboriginal experience in Canada, support and sustain Aboriginal aspirations and economies, help Canada live up to its treaty promises to Aboriginal peoples, and forge a truly post-colonial Canadian.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.038
GPT teacher head0.301
Teacher spread0.263 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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