An Institutional Approach to Bordering in Islands: The Canary Islands on the AfricanEuropean Migration Routes
Bibliographic record
Abstract
Islands play a significant role in international irregular maritime migration. Frequently they are part of maritime interstitial spaces between states, and their location, combined with institutional membership, makes them part of international migration routes and subject to border management strategies. In this paper borders are analysed as social institutions used for regulating relative permeability through rules of entry and exit for persons, goods and capital. Borders institutionalize territoriality and are politically implemented by states. They are selective, also in migration, and irregular border transit is not always indicative of an inability to control. The Canary Islands are used as an illustrative example of how border management at the southern edge of the European Union has evolved towards more coercive deterrence and tighter surveillance. The Canary Islands experienced irregular maritime immigration from the west African coasts during the first decade of the 21st century and most of these migrants intended to use the islands as transit space towards the European continent. Increasing surveillance in countries of origin, enforcement of border controls and stricter return policies were used to stop flows. The so-called “cayuco crisis” in 2006 induced institutional change in border management and forced the active involvement of the EU through FRONTEX.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".