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Record W2418704167 · doi:10.5539/jsd.v9n3p23

Farm Bill 2014: An Experimental Investigation of Conservation Compliance

2016· article· en· W2418704167 on OpenAlexvenueno aff
Hans J. Czap, Natalia V. Czap, Gary D. Lynne, Mark E. Burbach

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyIncentiveLegislatureCrowding outPublic economicsEconomicsBusinessCompliance (psychology)MicroeconomicsEnvironmental economicsPolitical scienceMonetary economicsPsychologySocial psychologyMarket economy

Abstract

fetched live from OpenAlex

Leading up to the 2014 Farm Bill, the House of Representatives and the Senate proposed alternative changes to the incentive structure for farmer conservation efforts. While both include crop insurance subsidies, the version proposed by the Senate made such subsidies conditional on conservation efforts. This study uses experimental methods to analyze the efficacy of these two alternative designs in comparison to the previous, 2008 Farm Bill, design and investigates in how far additional nudging for empathy can improve on the efficiency. The results support the contention that solely offering financial incentives, as is the case in the 2014 Farm Bill, leads to crowding-out of intrinsic motivations and hence may be counterproductive. Similarly, nudging for empathy by itself is relatively ineffective. Nudging in conjunction with financial incentives, however, has a statistically and economically significant and positive impact on conservation behavior and may therefore offer a relatively cheap way to improve the efficiency of conservation-related legislative efforts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.236
Teacher spread0.128 · 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 teacher head, 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

Citations5
Published2016
Admission routes1
Has abstractyes

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