Political-Economic Indicators for Self-Sustainability in Greenland
Bibliographic record
Abstract
The Northern Review 45 (2017): 93–111https://doi.org/10.22584/nr45.2017.006This article contributes to policy learning in Greenland by linking entrepreneurship and innovation system theory to recent and former attempts at creating political-economic indicators. The article shows how working methods have developed within the Greenland government where evidence-based governance is becoming more commonly used. The main findings indicate that the overall political objective of creating a self-sustaining economy is not possible in the short run, but is a politically meaningful overall goal. Existing measurements and indicators have been important instruments in developing an understanding of the connectedness of elements in the innovation system. Measurements and indicators could still be developed further, especially by extending with more individual level data that can be analyzed within the context of institutional level data. Also, the natural resource sectors need to be thought about in connection to other industries and the competencies of the population in order to avoid a resource curse. This could very well be done in a cross-sectional innovation policy, perhaps including an indicator for ”self-sustainability,” and combined with measuring the actual development in comparison with the set goals.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".