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The transient sand frontier: Senegal's moving sand procurement strategies

2025· article· en· W4406203718 on OpenAlexaff
Jean‐François Rousseau

Bibliographic record

VenueResources Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTransient (computer programming)FrontierProcurementBusinessEnvironmental scienceGeologyGeotechnical engineeringGeographyComputer scienceArchaeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.691
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.283
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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