SDG 12 needs an oceanic interface: sand mining, saltwater intrusion (SWI) and coastal sustainability
Bibliographic record
Abstract
Abstract The international development community has approached SDG 12 (Responsible Consumption and Production) through the lens of specific supply chains of consumer goods and services. For example, minerals from mines to markets; wood from forests to furniture; or food from farm to fridge, have been tracked in terms of their ecological profile in many of the SDG 12 targets. While such an approach can give us some idea of particular recycling or refurbishment opportunities, as well as waste-toenergy generation, it lacks a systems-oriented view on the interlinkages between socio-ecological systems of consumption and production. We argue that SDG 12 needs to be reimagined in terms of lateral impacts and connections in key sectors of resource extraction. Sand mining and saltwater intrusion (SWI) present an important example of how such a connection could be made between an anthropogenic activity in a coastal / marine environment and its ecological impact that could threaten food security. We present a review of research in this context that links these two seemingly disparate areas of academic inquiry. Focusing on the Mekong Delta we also consider how geospatial techniques could help to evaluate these connected impacts between sand mining and SWI and its consequential impacts on arable land and hence food availability and hunger. Considering a series of methodological challenges, we offer a way forward for measuring these impacts and charting a more integrative way forward for operationalizing SDG12 towards more sustainable environmental and social outcomes.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| 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".