Understanding Islandness Effects Through the Challenges of Water Infrastructure: A Case Study on the Kinmen Islands
Bibliographic record
Abstract
On Taiwan’s offshore islands of Kinmen, the water supply infrastructure has experienced various challenges stemming from the physical and social conditions of islands. These conditions are related to islands’ distinct characteristics, such as smallness, boundedness, and remoteness, collectively referred to as islandness. This paper combines perspectives from infrastructure and island studies to examine how islandness-related effects have contributed to infrastructure failures. Drawing from interviews and document analysis, it explores the issues faced by the reservoirs and the desalination plant in Kinmen, including small storage and poor water quality, lack of energy and technical capacities, high production and maintenance costs, and poor seawater quality. Then, based on the above case study and a document analysis of water supply technical reports on Taiwan’s offshore islands, some shared islandness-related conditions are identified and categorized in relation to smallness, remoteness and peripherality, and ocean materiality. While offering generalizability, it is crucial to note that these conditions are not seen as determined but rather as relational and contingent upon other factors. Through such examination, the paper contributes to the discussion surrounding islandness and infrastructure breakdowns, shedding lights on the relational effects of islandness on water supply infrastructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".