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Record W2161002879 · doi:10.1177/1090198108320357

Emergence and Robustness of a Community Discussion Network on Mercury Contamination and Health in the Brazilian Amazon

2006· article· en· W2161002879 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Education & Behavior · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsRobustness (evolution)Amazon rainforestSustainabilityCommunity resilienceCommunity networkIntermediaryComputer scienceSociologyEnvironmental healthComputer securityBusinessRedundancy (engineering)MedicineEcologyWorld Wide WebMarketing

Abstract

fetched live from OpenAlex

Information exchanges, debates, and negotiations through community social networks are essential to ensure the sustainability of the development process initiated in participatory research. The authors analyze the structural properties and robustness of a discussion network about mercury issues in a community in the Brazilian Amazon involved in a participatory research aimed at reducing exposure to the pollutant. Most of the villagers are connected in a large network and are separated from other individuals by few intermediaries. The structure of the discussion network displays resilience to the random elimination of villagers but shows vulnerability to the removal of one villager who has been a long-term collaborator of the project. Although the network exhibits a structure likely to favor an efficient flow of information, results show that specific actions should be taken to stimulate the emergence of a pool of opinion leaders and increase the redundancy of discussion channels.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.368
Teacher spread0.335 · 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