Local (or Not) Insecurity on Arctic Twitter/X: Global Insecurity and Climate Change
Bibliographic record
Abstract
An advance online version of this article was first published September 2024.While Twitter, now known as X, has been used to study political sentiments around elections and political discourse broadly speaking, less research has explored questions of insecurity. Using a data set of Arctic tweets between 1 January 2020 and 31 March 2023, and the R programming language, I asked how posts regarding this region framed the debate around insecurity. My work finds that spikes of insecurity on Arctic Twitter/X did not directly correlate with moments of global insecurity such as the Russian invasion of Ukraine in 2022 or the COVID-19 pandemic from early 2020. Instead, they reference environmental insecurities such as the 2020 Norilsk oil spill in Russia and other Arctic-specific events that almost all have to do with climate change, both locally and globally. These findings suggest that similar to public opinion polls, local insecurities have more resonance with Arctic publics, rather than highly politicized moments of global insecurity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".