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Record W2805641839 · doi:10.4337/9781782540601.00017

Social capital and place-based policy: Aboriginal communities in Canada

2013· book-chapter· en· W2805641839 on OpenAlexaboutno aff
M. Rose Olfert, David Natcher

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2013
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDiseconomies of scaleContext (archaeology)Competition (biology)Economic geographyCapital (architecture)Economies of scaleLaggingFujita scaleCorporate governanceMetropolitan areaMarket economyGeographyMicroeconomics

Abstract

fetched live from OpenAlex

Public policies targeting local or regional economies are typically the result of concerns regarding local outcomes that are lagging relative to a (national) reference point. Spatially uneven economic activity is explained, in neoclassical economics, by differences in natural advantages and factor endowments. However, other factors including notably local institutions have also been identified as being of major importance, especially in the context of developing countries (Acemoglu and Robinson 2008; Hall et al. 2010; North 2003). Over time, regional performance will change as a result of both external (demand for exports, new technologies) and internal (governance and institutions) factors. The New Economic Geography (NEG) that developed following early work by Krugman (1991) provided a framework for understanding the possible divergence of core and peripheral regions under conditions of imperfect competition, falling transportation costs, increasing returns to scale, and mobility of capital and labor (Krugman 1991; Fujita and Krugman 2004; Tabuchi et al. 2005). Market potential (demand) plays a very central role in this perspective and, in particular, the demand for variety. Further, strong path dependence may mean that regions with existing advantages are long term net recipients of labor and capital flows, perpetuating and enhancing their competitive advantage (Head and Mayer 2004).

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.851

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 studies0.0000.000
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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
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
Published2013
Admission routes1
Has abstractyes

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