Towards the enhancement of Arctic digital industries: 'Translating'cultural content to new media platforms
Bibliographic record
Abstract
This paper proposes a preliminary framework for digital 'translation' attempting to, (while cognisant of conceptual limitations embedded in this model) localise aspects of Inuit knowledge, culture and IQ (in the sense of Inuit Qaujimajatuqangit) into digital artifacts for new generations of Inuit and non-Inuit learners. In addition to delineating challenges and opportunities based on theoretical models and actual projects currently underway in Nunavut and Nunavik, it proposes developing Arctic digital industries through convergent cultural media. Finally, it encourages US and Canadian governments during this four-year North American governance cycle of the Arctic Council (two years each for Canada (2013-2015) and the United States (2015-2017) to invest in digital infrastructure, from both a humanistic (via training) and technological perspective. Conceptually, the article argues that culturally focused circumpolar digital development is fundamental to fulfilling the language of the Canadian and US Arctic Strategies, indicating the importance of validating the cultures and voices of the 'People of the North'. It warns against potential post-colonial dangers inherent in digital training, and concludes by arguing that based on current increased global focus on the resources and geo-strategic possibilities inherent in the Arctic (accelerated by global warming and augmented militarisation of the North), that the time is pivotal to ensure that digitally localised and disseminated voices of the Inuit and circumpolar indigenous voices are available electronically in the widest possible variety of media forms.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".