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Record W2891851733 · doi:10.1089/elj.2018.0520

Beyond Party Financing: The Rise of Political Ethics Regulation in Canada and France

2018· article· en· W2891851733 on OpenAlexaffabout
Luc Juillet, Éric Phélippeau

Bibliographic record

VenueElection Law Journal Rules Politics and Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsScrutinyPoliticsLegislaturePolitical scienceLanguage changeCompromisePublic administrationCampaign financeGovernment (linguistics)Political economyEnforcementLegislationLawEconomics

Abstract

fetched live from OpenAlex

While the need to fund election campaigns may lead candidates to compromise their integrity by obtaining illegal funding or using their public office to “sell” favors to wealthy donors, attempts to influence sitting public officials through illegal or unethical means often do not involve party financing. In politics and government, private and public interests intersect in myriad ways in diverse settings, often bearing the potential of unethical behavior and corruption. From this perspective, many countries have adopted more comprehensive policy frameworks to regulate political ethics since the 1970s. Comparing the history of those reforms in Canada and France, we looked for similarities and differences in the factors that triggered legislative changes, the timing and politics of these reforms, as well as some of the characteristics of the adopted measures. We show that 1) there has been a significant expansion of the legislative framework regulating political ethics in both countries over this period, especially over the last 15 years; 2) the combination of media scrutiny, scandals, and party competition has been an essential trigger for reforms in both countries; 3) in addition to politicians, new nongovernmental integrity advocates and expert commissions have played a significant role in the politics of the reform of the last 15 years; and 4) while the regulatory frameworks currently in place in both countries are much more elaborate and intrusive than in the past, they still comprise few possibilities of sanctions and suffer from weak and uneven enforcement.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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.

Study designTheoretical or conceptual
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

Citations4
Published2018
Admission routes2
Has abstractyes

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