Creative Alaska: creative capital and economic development opportunities in Alaska
Bibliographic record
Abstract
ABSTRACT The flaws of the 20th century–type development ‘mega–projects’ in the circumpolar North prompt Arctic regions actively to search for alternative strategies of regional development that break away from resource–dependency and reconcile local (traditional) societies with the realities of post–Fordism and globalisation. This paper presents a study that focuses on the notion of creative capital (CC) and assesses its ability to foster economic development in Alaska. The findings suggest that some characteristics of the CC observed in Alaskan communities are similar to those found in southern regions, whereas others are distinct (but similar to those in the Canadian North). In Alaska, the synergy between cultural economy, entrepreneurship and leadership appear to be more important in characterising creative capacities than formal education. The geographical distribution of the CC is uneven and heavily clustered in economically, geographically and politically privileged northern urban centres. However, some remote regions also demonstrate considerable levels of creative potential, in particular associated with the aboriginal cultural capital (artists, crafters, etc.). A number of Alaskan regions, creative ‘hot spots’, could become places that can benefit from alternative strategies of regional development based on CC, knowledge–based and cultural economies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".