Making and Unmaking Airports in Tunu (East Greenland): The Socio-Material Dynamics of Hope and Connectivity
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".