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Flying into Frictioned Futures: Development of Canada’s Northernmost Runways

2025· article· en· W4416193725 on OpenAlexvenueaboutno aff
Katrin Schmid

Bibliographic record

VenueAnthropologica · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsRunwayFutures contractService (business)Jet (fluid)Transport infrastructure

Abstract

fetched live from OpenAlex

In Nunavut, Canada’s largest, youngest, and northernmost territory, gravel, asphalt, and concrete determine much of daily life. Airport runways’ materialities dictate the types of aircraft that can land in each of the 25 fly-in communities and with them the cargo-carrying capacity, passenger mobility, and frequency of intercommunity connections. The last jet capable of landing on gravel was recently phased out of commercial service in Nunavut, a move that further limits access to communities and works counter to desires voiced by residents to increase jet access. Temporality, an immaterial concept, becomes intimately articulated through the physical realities of transport infrastructure in Nunavut. I examine the interplay of residents’ imagined futures for their communities and the on-the-ground reality of developing, operating, and maintaining gravel and paved runways in Nunavut as points of friction, following Anna Tsing. I argue that the divergent development of communities can be partially attributed to the accessibility of transport infrastructure in each location. In conclusion, I question the idea of infrastructure as a promise of a “future perfect” (Hetherington 2016) and attempt to refocus the processes of Nunavut’s transport infrastructure development onto Nunavummi-centred solutions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.1390.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.376
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Science and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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