MétaCan
Menu
Back to cohort
Record W4402640211 · doi:10.7202/1113393ar

Making and Unmaking Airports in Tunu (East Greenland): The Socio-Material Dynamics of Hope and Connectivity

2023· article· en· W4402640211 on OpenAlexvenueno aff
Sophie Elixhauser

Bibliographic record

VenueÉtudes/Inuit/Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsDynamics (music)GeographyHistoryEconomic geographyOceanographyGeologySociology

Abstract

fetched live from OpenAlex

Like many airports throughout the Arctic, Kulusuk Airport, the entrance to the sparsely populated East Coast of Greenland, is built on the remnants of past military activities and is located some distance from the regional capital, Tasiilaq. For years, there have been discussions regarding the construction of a new airport in Tasiilaq to improve connectivity and reduce dependence on helicopter flights. Throughout the East Coast, many residents feel that they are looked down upon by the (West) Greenlandic population and are given little priority in the political and economic decisions taking place in the faraway national capital of Nuuk, which feeds into residents’ attitudes towards the ever-suspended airport plans. Many residents place great hope on this plan, as this “infrastructural hope” (Reeves 2017) includes economic and social possibilities and an improvement of the region’s status both within the country and abroad. On the other hand, in the village of Kulusuk, near the current airport, people fear the repercussions of this new airport. I explore the hopes, fears, and affect generated by and embedded within infrastructure, considering issues of remoteness, social and physical connectivity, “infrastructural violence” (Rodgers and O’Neill 2012), and residents’ future imaginaries and historical experiences in (post)colonial Greenland. Describing the socio-material dynamics of hope and connectivity, this article shows how aviation infrastructure is never just about the physical infrastructure but is always enabled by and embedded in societal processes.

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

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.334
Teacher spread0.284 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueÉtudes/Inuit/StudiesSame topicWater Governance and InfrastructureFrench-language works237,207