SDG 12 needs an oceanic interface: sand mining, saltwater intrusion (SWI) and coastal sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it