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Toward a Global Agenda for Research in Environmental Epidemiology

2006· article· en· W2069959223 on OpenAlexaff
Colin L. Soskolne, Colin D. Butler, Carel IJsselmuiden, Leslie London, Yasmin von Schirnding

Bibliographic record

VenueEpidemiology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSustainabilitySummitPolitical scienceEnvironmental ethicsMillennium Development GoalsGlobal healthEconomic growthDevelopment economicsPovertyGeographyHealth careEcologyEconomics

Abstract

fetched live from OpenAlex

The global environment is in critical decline. Whether one's concern about environmental epidemiology stems from the perspectives of environmental health, climate change, ecological collapse, or growing inequity, clear problems exist. Natural capital resources are being depleted; disregard for the integrity of ecosystems is entrenched in current business practices. Indeed, despite increasing rhetoric to the contrary, the disregard displayed by those who hold power globally toward long-term sustainability and, thus, the health and well-being of future generations, could be described as wanton. Six years ago, the Millennium Development Goals were announced by the United Nations as a rallying point for action to achieve a sustainable future, particularly by reducing the gap between the "have mores" and "have nots." The attainment of these Goals is now endangered, as is, apparently, the spirit of optimism and idealism that inspired them at the Millennium Summit. We call for a reinvigoration of both concern about-and action on-sustainability. In particular, we appeal to those engaged in the field of environmental epidemiology (and other specialties with whom they engage) to consider how they might help by incorporating sustainability issues (including global ecological integrity and global environmental justice) into their own research programs. This incorporation would make a vital contribution to protect both present and future generations and to reduce resource and health gaps between North and South. Simply put, we propose that sustainability becomes integral to advancing the science of environmental epidemiology and related environmental disciplines.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.504
GPT teacher head0.500
Teacher spread0.004 · 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; both teacher heads agree on what is shown here.

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

Citations30
Published2006
Admission routes1
Has abstractyes

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