Social capital and place-based policy: Aboriginal communities in Canada
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".