Sustainably managing natural resources and the need for construction materials in <scp>P</scp>acific island countries: The example of <scp>S</scp>outh <scp>T</scp>arawa, <scp>K</scp>iribati
Bibliographic record
Abstract
Abstract The growing demand for construction materials in S outh T arawa, a remote atoll in the S outh P acific, provides an example of the environmental and social challenges associated with the use of non‐renewable resources in the context of small island countries threatened by coastal erosion and climate change. In many small P acific island countries, the availability of construction materials is limited, with the majority mined from beaches and coastal reefs in an unsustainable manner. Growing demand for construction aggregates is resulting in more widespread sand mining by communities along vulnerable sections of exposed beach and reefs. This has serious consequences for coastal erosion and impacts on reef ecosystem processes, consequences that cannot be easily managed. Construction materials are also in high demand for infrastructure projects which are financed in part with support from international development agencies and donors. This paper reviews the various challenges and risks that aggregate mining poses to reefs, fish, and the coastal health of S outh T arawa and argues that the long term consequences from ad hoc beach/reef mining over large areas are likely to be far greater than the impacts associated with environmentally sustainable, organized extraction. The paper concludes with policy recommendations that are also relevant for neighbouring island countries facing similar challenges.
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 | 0.001 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".