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Record W7098264957

An Archaeological-Genealogical Analysis of Public Health Discourse on Lead: Reformulating lead-based paint as a problem in Canada

2016· article· en· W7098264957 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicColeoptera Taxonomy and Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsLead poisoningPublic healthSuicide preventionLead (geology)Occupational safety and healthLead exposureGovernment (linguistics)Human factors and ergonomics
DOInot available

Abstract

fetched live from OpenAlex

Lead is a serious developmental neurotoxin with the capacity to interrupt brain development and impair functioning. Since at least 1930 numerous case studies in American, Canadian and Australian literature have identified lead based paint in the home as a source of poisoning for young children; and since at least 1990 evidence has shown that it is the lead dust from deteriorating paint in older homes and renovating activities that is the primary source of chronic exposure for young children today. Not much is known about the extent of childhood lead poisoning in Canada. Gaps in our understanding include a lack of national survey data on childhood blood lead levels and an absence of reliable data to determine the era of housing that poses the greatest risk. This thesis posits that despite this paucity of research knowledge there is evidence to suggest that populations of vulnerable children continue to be harmed by exposure to historic sources of lead, such as lead-based paint found in older housing stock. This thesis examines the evidence to support this contention

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.011
Science and technology studies0.0370.019
Scholarly communication0.0090.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.272
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same topicColeoptera Taxonomy and DistributionFrench-language works237,207