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Record W4252847217 · doi:10.32920/ryerson.14656959.v1

Compensation in hazardous facility siting : ban analysis of compensatory agreements

2021· preprint· en· W4252847217 on OpenAlexfundno aff
Marta Wrzal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
FundersCanadian Nuclear Safety CommissionMinistry of Education, IndiaEuropean Synchrotron Radiation Facility
KeywordsCompensation (psychology)Hazardous wasteProcess (computing)BusinessCapital (architecture)EconomicsComputer scienceEngineeringGeography

Abstract

fetched live from OpenAlex

Although theoretically the voluntary site strategy has been commended for its success at solving local community problems, there has been a small number of siting successes actually achieved. This study investigates the approach of negotiated compensation and reward in the collaborative process under which willing individuals can come to an agreement concerning the siting of a noxious facility. Elaborating upon Kuhn and Ballard's (1998) optimistic conclusions regarding the progress of facility siting approaches in North America, the study investigated the true nature of collaborative theory in a case analysis of environmentally hazardous facility projects. The result suggest the compensation is an effective tool in the siting process. The analysis indicates that there has been an evolution in the nature of community agreements over the last ten years into more sophisticated allocations of benefits and burdens. The study also concludes that direct costs allocated by proponents for the purpose of compensation remain low and relatively small when compare to the estimated initial capital of the projects.

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)0.008
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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