From Transport Development to Land Reclamation: Contested Spatio-Temporalities along Penang’s Littoral, Malaysia
Bibliographic record
Abstract
“One island is enough” was written on a banner shaped like Penang Island, Malaysia, during a demonstration against the plan to create three artificial islands. The Penang South Reclamation (PSR) project, adopted by the local administration and developers, aims to finance the Penang Transport Master Plan (PTMP). This ambitious plan includes several components, such as monorail and light rapid transit lines. While land reclamation is not new in Penang, this mega-development project has faced unprecedented opposition from a wide range of actors. In dialogue with fishers and activists supporting the Penang Tolak Tambak (Penang Rejects Reclamation) campaign, and through the analysis of government documents, non-governmental reports, news articles, and social media, this paper traces how state, corporate, and civil society actors have shifted their focus from the PTMP to the PSR project. Thus far, scholarly literature on island environmental movements has focused on the right to the island and the right to nature, while claims related to the land-sea interfaces, which so clearly delineate island spatialities, have been somewhat neglected. By exploring the contested spatio-temporalities of this land reclamation project off the south coast of Penang, this paper expands the dialogue of the right to the island to include the right to the sea.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| 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".