Bibliographic record
Abstract
During the global Covid-19 pandemic, households, communities and regions around the world were faced with the hardening of borders at a variety of jurisdictional and spatial levels. These policy actions saw a sharp rise in insularisation occur as border geographies spurred insularity. The purpose of this paper is to examine this phenomenon and explore how this insular imagery took hold. Jurisdictional islanding in the form of “Covid-islands” and “Covid-archipelagos” is introduced and explained as policy constructs which occurred at both micro and macro levels during the Covid-19 pandemic. This paper then examines Eastern Canada’s Covid-archipelagic “Atlantic Bubble”, constructed by the joint-islanding of the Canadian provinces of New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland and Labrador, as an illustrative case study example. The paper finishes by analysing the sociospatial and temporal dynamics of Covid-islands and Covid-archipelagos, tying to dimensions of culture, territory and society intercon-nected amongst prior concepts and paradigms of island understanding. Islanding in the Covid-19 era brought us back to the notion of seclusion and detachment that in a way echoes the paradigms that had already been deconstructed in the field of island studies. However, the emergence of these sociospatial island imaginaries leads us to rethink insularisation and what it meant to be insularised in the Covid-19 period.
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 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 | 0.000 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".