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Record W2400378036 · doi:10.1177/1048291116651726

Occupational and Environmental Health Risks Associated with Informal Sector Activities—Selected Case Studies from West Africa

2016· article· en· W2400378036 on OpenAlexaff
Niladri Basu, Paul Ahoumènou Ayelo, Luc Djogbénou, Marius Kêdoté, Hervé Lawin, Honesty Tohon, Elizabeth O. Oloruntoba, Nurudeen A. Adebisi, Danielle Cazabon, Julius N. Fobil, Thomas G. Robins, Benjamin Fayomi

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcGill University
FundersFogarty International CenterNational Institutes of Health
KeywordsEnvironmental healthOccupational safety and healthBusinessGeographyMedicine

Abstract

fetched live from OpenAlex

Most in the Economic Community of West African States region are employed in the informal sector. While the informal sector plays a significant role in the region's economy, policymakers and the scientific community have long neglected it. To better understand informal-sector work conditions, the goal here is to bring together researchers to exchange findings and catalyze dialogue. The article showcases research studies on several economic systems, namely agriculture, resource extraction, transportation, and trade/commerce. Site-specific cases are provided concerning occupational health risks within artisanal and small-scale gold mining, aggregate mining, gasoline trade, farming and pesticide applications, and electronic waste recycling. These cases emphasize the vastness of the informal sector and that the majority of work activities across the region remain poorly documented, and thus no data or knowledge is available to help improve conditions and formulate policies and programs to promote and ensure decent work conditions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.442
Teacher spread0.309 · 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.

Study designObservational
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

Citations21
Published2016
Admission routes1
Has abstractyes

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