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Record W2792284013 · doi:10.1111/polp.12239

Policy Transfer and Diversification in Resource‐Dependent Economies: Lessons for Kazakhstan from Alberta

2018· article· en· W2792284013 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePolitics &amp Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)PoliticsIndustrialisationPolitical scienceCapitalismPublic policyIndustrial policyEconomicsEconomyEconomic growthBusinessInternational tradeMarket economy

Abstract

fetched live from OpenAlex

Abstract Economic diversification in resource‐dependent countries is a difficult process. Most of these countries’ efforts to diversify have been unsuccessful. Alberta is one exception. It has succeeded in diversifying its economy by “diversifying in energy” using explicit policy decisions to promote human capital development in science, technology, engineering, and math (STEM), complement STEM education with education in management and innovation, foster proper market discipline, establish an effective intellectual property rights system, and strengthen links between various industries to support innovation. Kazakhstan's policy makers can learn valuable lessons from Alberta's successes. It is also important to understand that some of the initiatives that were successful in Alberta may not be appropriate for Kazakhstan because of the latter's system of state‐guided capitalism and limited public service capacity. Finally, evidence from Alberta suggests that promoting “winning” sectors is a way for favored insiders to capture a share of resource rent and seldom succeed. Related Articles Khodr , Hiba , and Isabella Ruble . 2013 . “.” Politics & Policy 41 (): 656 ‐ 689 . http://onlinelibrary.wiley.com/doi/10.1111/polp.12033/full Khodr , Hiba . 2014 . “.” Politics & Policy 42 (): 271 ‐ 310 . http://onlinelibrary.wiley.com/doi/10.1111/polp.12068/full Kim , Hae S . 2017 . “.” Politics & Policy 45 (): 83 ‐ 104 . http://onlinelibrary.wiley.com/doi/10.1111/polp.12190/full Related Media . 2003 . “Should Developing Country Industrialisation Policies Encourage Processing of Primary Commodities?” The ACP‐EU Courier 196: 30‐32. http://ec.europa.eu/development/body/publications/courier/courier196/en/en_030.pdf Litan , Robert E ., and Carl J. Schramm . 2007 . “Good Capitalism, Bad Capitalism, and the Economics of Growth and Prosperity.” Council on Foreign Relations . https://www.cfr.org/event/good-capitalism-bad-capitalism-and-economics-growth-and-prosperity . 2015 . “Economic Diversification and Nonextractive Growth.” In Engagement in Resource‐Rich Developing Countries: The Cases of the Plurinational State of Bolivia, Kazakhstan, Mongolia, and Zambia . Washington, DC: World Bank. 28‐42. http://ieg.worldbankgroup.org/sites/default/files/Data/reports/chapters/ccpe-synthesis_ch4.pdf

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.965

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 communication0.0000.000
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 head0.047
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