Peopling the State: Arctic State Identity in Norway, Iceland, and Canada \n
Bibliographic record
Abstract
As increasing levels of attention are directed northwards to the rapidly changing Arctic region, states and stakeholders from near and far position themselves in anticipation of what is yet to come – challenges and opportunities, Arctic futures. For the eight Arctic states with territory north of the Arctic Circle, this has prompted new emphasis on their ‘Arctic identities’: political claims of homelands and histories through which formal credibility and authority are consolidated and normalised. However, as a space that has often been imagined in terms of distances, frontiers, ice, cold, and snow, Arctic identity narratives are a matter of re-interpretation, re-negotiation, and re-imagination of the ‘nation-state’, who and where ‘we’ are. \n \nWhile emotive statements of identity may or may not resonate with electorates, what has hitherto been less explored is how these work within the state itself to condition political practice. That is, how a formal title of Arctic statehood is understood, related to, and subsequently enacted by those tasked with its everyday performance – indeed, the everyday practices through which the ‘Arctic state’ emerges as such. Recognising the state as an idea(l) that only ‘materialises’ as an effect of practice arguably necessitates attention to those performing said practices – state personnel. \n \nTo this end, I here introduce the concept of ‘state identity’ discourses in order to explore how state representatives’ articulations of identity are bounded in spatiotemporal terms, and yet, are always relational; the Arctic state comes about through encounters at all scales of interaction, from the international to the intimately personal. With reflections from state representatives in three of the eight Arctic states – Norway, Iceland, and Canada – I argue that we need to acknowledge the numerous subjectivities, stories, and relations through which the Arctic state comes into being, thereby ‘peopling’ the state. \n
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".