The transient sand frontier: Senegal's moving sand procurement strategies
Bibliographic record
Abstract
This article probes the causes and implications from the displacement of construction sand procurement strategies from coastal areas to inland sand dune ecosystems in Senegal. It retraces how the gradual implementation of a beach sand mining ban triggered a transient sand frontier process in sand dune ecosystems that now sustain rising sand needs driven by rapid urbanisation in the coastal cities of Dakar and Saint-Louis. Vertical and horizontal limits to sand quarrying in the sand dunes lead to the extractive frontier constantly moving farther away from urban and periurban sand consumption sites. The resulting transient frontier process complements documented sand frontier scenarios where spatial extension, or commodity widening, combines with intensifying extractivism, or commodity deepening. In coastal Senegal, spatial extension rather proceeds in tandem with frontier closure. The sand transient frontier yields sand supply and price pressures that create challenges to Senegal development ambitions, most specifically those that entail the expansion of the concrete dependent affordable and premium city models. The development minerals agenda has so far proved insufficient to yield the discursive shift required for elevating sand supply as key to the achievement of development programs in Senegal. Connecting sand, the development minerals and the ‘strategic’ or ‘critical’ minerals agendas could help elevate sand supply-related challenges among policymakers' priorities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".