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Record W1702322409

Socio-Economic Trends in the Canadian North: Comparing the Provincial and Territorial Norths

2014· article· en· W1702322409 on OpenAlexaffabout
Chris Southcott

Bibliographic record

VenueNorthern review · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsLakehead University
Fundersnot available
KeywordsCensusPopulationGeographyPovertyResource (disambiguation)BoomSocioeconomicsEconomic growthDemographic economicsDemographyEconomicsSociology
DOInot available

Abstract

fetched live from OpenAlex

While there has been a recent increase in social research relating to the Canada’s Territorial North, there is a relative poverty of research dealing with the Provincial North. That comparatively little has been written about the Provincial North means it is difficult to compare the social and economic situations in these two regions. This article is an introductory comparison of key socio-economic indicators as contained in the Census of Canada. The data shows that there are both similarities and important differences between these two regions. In addition to the Provincial Norths having a much larger population than the Territorial North, the two regions have different occupational and industrial structures with the Provincial Norths having more blue-collar jobs linked to the resource sector while employment in the territories is much more dependent on the public sector. Despite this, in terms of population change, both regions appear to be very much influenced by the booms and busts of the resource economy. Both regions have higher percentages of Aboriginal population than most regions in Canada. Indeed, differences between the various regions of the Canadian North are likely the result of variations in the percentage of the population that is Aboriginal.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.998

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 studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.354
Teacher spread0.307 · 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 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
Published2014
Admission routes2
Has abstractyes

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