Handbook for social licence to operate in Arctic industries
Bibliographic record
Abstract
This handbook is designed for industrial actors in the Arctic regions and decision-makersinvolved in strategic planning for Arctic industrial development. It guides the assessmentand implementation of Social Licence to Operate (SLO). The handbook begins with conciserecommendations, explores the concept of SLO, and discusses the relevance of theserecommendations in the Arctic.The SLO concept addresses the relationships between local communities and industriessuch as mining, aquaculture, tourism, and forestry in the Arctic. These industries can havesignificant local environmental and social impacts while generating substantial benefitsbeyond the local communities, leading to potential controversies.The SLO approach aims to gain the acceptance and trust of local communities,which is increasingly important. Failure to achieve this can result in significant costsdue to project disruption or termination. The aim is to clarify the understanding andinterpretation of the SLO concept and how companies and stakeholderscan work to enhance the SLO of an industry.Originally developed in the mining industry, the ArcticHubs project explores whetherthe SLO concept can be adapted to other industries like aquaculture, forestry, and tourism.ArcticHubs project has also examined how these industries impact Sami reindeer herderand Greenlandic Inuit hunters and fishermen through SLO activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.036 |
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".