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Record W2808353657

Turning to the Source: Assessing the Evidence Sources Used to Describe the Potential Human Health Impacts of Wind Turbines by Public Health Organization Websites and Community Group Websites Using a Social Network Analysis Approach

2018· dissertation· en· W2808353657 on OpenAlexaboutno aff
Nicholas Charlie Brandon

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthWind powerSocial network analysisPublic relationsCommunity healthSocial mediaBusinessData scienceEngineeringPolitical scienceAdvertisingWorld Wide WebComputer scienceMedicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Wind turbines are a source of renewable energy that has become more common in Canada in the past decades. Concerns have been raised over potential adverse health effects from exposure to wind turbines, particularly wind turbine noise. A disagreement exists over the potential harm from exposure to wind turbines to human health, where many public health organizations state that there are no direct human health impacts from wind turbine exposure, while many community groups state that wind turbines are harmful to human health. \n \n \nObjectives: 1. Determine the types of evidence cited by community group websites, and by public health organization websites, to support their respective positions on the potential health effects of wind turbines; and 2. Assess the pattern of citations or links to the evidence used by community groups and public health organizations to characterize and interpret these patterns of evidence citation and to see whether and how these patterns differ between the two groups. \n \nMethods: Websites of Canadian community groups, public health organizations, environmental non-governmental organizations (eNGOs) and academic organizations were identified using an Internet search strategy. The identified websites with content on wind turbines and human health that met the inclusion criteria were characterised with a data collection tool to gather information about the webpage structure and its links to evidence sources and other organizations’ websites. Descriptive statistical analysis was performed on the website characteristics and evidence and organization citation data. Testing for significant differences between community groups and public health organizations was done using t-tests and chi-squared tests. Adjacency matrices were created to represent the presence of ties between organization websites and between organization websites and evidence sources. Graphs (sociograms) were created based on the adjacency matrices to visualise the relationship between the different types of organizations as well as between organizations and evidence sources. Additional centrality measures were calculated for the visualised networks and representations of structural equivalence were created to determine whether nodes in the network were similar. \n \nResults: 67 identified websites met the inclusion criteria: 2 academic organizations (3%), 6 eNGOs (9%), 18 public health organizations (27%) and 41 community groups (61%). Significant differences were found between community group websites and public health organization websites in their position on wind turbines and human health, and the presence of website components (social media or a news section). Community group websites were significantly more likely to cite blogs, news, video evidence, and personal accounts/testimony than public health organization websites, but no significant difference was found in the citation of peer-reviewed literature or grey literature. Significant differences for mean citation counts between community group websites and public health organization websites were found for experimental studies with controls, grey literature, and observational study without controls. Community group websites predominantly linked to other community group websites and public health organization websites predominantly linked to government and other public health organizations websites. \n \nSocial network analysis of the 67 Canadian organization websites determined that websites tended to link to other organization websites of the same organization type. The network structure lacked a central node and was divided according to the websites’ position on whether wind turbines were potentially harmful to human health—where websites within the network clustered by position. There was structural equivalence between organization websites by organization type, where certain national and provincial websites had similar roles within the network. \n \nThe results from examining the network between the 67 Canadian organization websites and the 584 evidence sources identified differences in the specific evidence sources and types of evidence that were cited. When the network analysis was limited to evidence sources with more than two citations, the evidence citations were found to be similar in type (reviews, grey literature and cross-sectional surveys) but varied by the specific evidence source cited. The type of grey literature cited varied by organization type, where community group websites tended to cite grey literature that originated from community groups and public health organization websites tended to cite grey literature that originated from public health organizations, government or industry. Higher quality evidence sources were shared between websites across the organization types, but the lower quality evidence sources citations were predominantly shared between organization websites of the same type. \n \nConclusions: The network of Canadian organization websites with content on wind turbines and human health was structured according to organization type and position on potential health effects. Grey literature, reviews and cross-sectional surveys were the most frequently cited evidence sources and evidence citation patterns differed by organization type. These results provide a basis for understanding which types of evidence sources are used to substantiate positions on wind turbines and human health and how public health practitioners and researchers can approach the uncertainties in the evidence base on the topic.

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)0.065
metaresearch head score (Gemma)0.368
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.368
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0660.054
Science and technology studies0.0020.002
Scholarly communication0.0080.005
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.041
GPT teacher head0.299
Teacher spread0.257 · 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.

Study designObservational
DomainEvaluation
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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