Bibliographic record
Abstract
This article is a critical study of the planning and design process of the Sustaining Arctic Observing Network (SAON). SAON, in its ambition to build a comprehensive, pan-Arctic monitoring system, seeks to integrate all relevant scientific and environmental monitoring sites in the Arctic, guided by an ethic of inclusion regarding the knowledge of indigenous Arctic peoples (KIAP). It is argued that the logics of inclusion in play, paradoxically, risks limiting the capacity for Arctic indigenous peoples to control their knowledge and its uses, to monitor the activities and outputs of SAON itself, and to appropriate the SAON system and its data for uses they control. This article also suggests an alternative approach: rather than place KIAP within SAON, it calls for planners to consider establishing knowledge relations between SAON and KIAP so that the distinct status of KIAP—in a position of exteriority to the comprehensive monitoring system—is acknowledged. Within these knowledge relations, differences in the production of knowledge can be effectively recognized, a site can be created for reviewing SAON’s monitoring work by local communities and practices, and strategies for open, adaptable data systems for local users can be established.
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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 | 0.053 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.085 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".