(Un)Making smallness: Islands, spatial ascription processes and (im)mobility
Bibliographic record
Abstract
Official categorization systems classify some states, including island-states, as small. Malta, located in the Mediterranean Sea, is one of six European microstates and the European Union’s smallest member state. Smallness, however, refers to more than fixed geographic scales. The understanding of smallness developed in this article, in contrast, moves beyond geographic features and argues instead that smallness is related to perceptions, experiences, and ascriptions. We challenge universal understandings of smallness against the backdrop of ethnographic research carried out in Malta (2013–2018) by exploring how island-related smallness is produced and used situationally in the context of (im)mobility. By focusing on narratives of smallness by various actors with whom we engaged during fieldwork, we demonstrate how smallness, islandness, and (im)mobilizing policies intersect at the EU’s external border. In addition, and in order to contribute to decolonial perspectives within Island Studies, we reflect on our role in (un)making smallness. We discuss our understandings of smallness against the backdrop of our empirical material and scholarly debates, and, in so doing, avoid the reproduction of universal understandings. In this vein, we argue for the deconstruction of simplified interpretations of smallness and claim that smallness must be viewed as a relational concept.
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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 | 0.004 | 0.009 |
| 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 | 0.029 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.008 |
| 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".