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Record W2907180408

Illuminating Indigenous Economic Development

2018· preprint· en· W2907180408 on OpenAlexaboutno aff
Donna Feir, Rob Gillezeau, Maggie Jones

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousProxy (statistics)GeographyPer capitaContext (archaeology)Sample (material)Per capita incomeEcologyComputer scienceBiologySociologyDemography
DOInot available

Abstract

fetched live from OpenAlex

There are over 1, 000 First Nations and Inuit communities in Canada. Only 357 of these communities are consistently included in the most comprehensive public data source on economic activity, the Community Well-Being (CWB) Database. We propose using nighttime light density measured by satellites as an alternative indicator of well-being. We show that nighttime light density is an effective proxy for per capita income in the Canadian context and provide evidence that existing publicly available databases on well-being consist of heavily selected samples that systematically exclude many of the least developed communities. We show that sample selection into the publicly available data can lead to incorrect conclusions based on three applications: (i) the comparison of well-being across community types over time; (ii) an analysis of the historical and geographic determinants of economic activity in Indigenous communities; and (iii) a study of the effects of mining intensity close to Indigenous communities. Based on these applications, we suggest that using nighttime light density overcomes the biased selection of communities into the publicly available samples and, thus, may present a more complete picture of economic activity in Canada for Indigenous peoples. JEL Classification: I15, J15, J24

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.032
GPT teacher head0.312
Teacher spread0.280 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicImpact of Light on Environment and HealthFrench-language works237,207