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Record W2095354690 · doi:10.1080/13549830500203246

Public involvement processes, conflict, and challenges for rural residents near intensive hog farms

2005· article· en· W2095354690 on OpenAlexaboutno aff
J. Mackenzie, Naomi Krogman

Bibliographic record

VenueLocal Environment · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsRural developmentEconomic growthPolitical scienceSocioeconomicsEnvironmental planningEnvironmental resource managementBusinessGeographyDevelopment economicsPublic relationsSociologyAgricultureEconomics

Abstract

fetched live from OpenAlex

Large-scale hog operations have generated intense conflict in rural communities in North America, and more recently in Alberta, Canada. The structure and process of public involvement processes for intensive hog operations influences how well the core issues of concern are addressed by public officials and pork producer proponents. This study reports interview findings from 43 key players in intensive hog operation controversies across four cases in Alberta, and summarizes four inter-related areas of the public participation processes that affect levels of trust among stakeholders, thereby altering the perceived fairness and the balance of power: (1) the timing of the public participation process; (2) information sharing; (3) accessibility of the process; and (4) accountability of public institutions and policies. Recent changes to the regulations of intensive livestock operations will unlikely address core issues of conflict without incorporating the above elements of democratic public involvement processes.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.122
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.006
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.236
Teacher spread0.138 · 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

Citations14
Published2005
Admission routes1
Has abstractyes

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