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Record W6928906534 · doi:10.3886/e130285v1

Data and Code for: "Money and Politics: The Effects of Campaign Spending Limits on Political Entry and Competition"

2022· dataset· en· W6928906534 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueICPSR Data Holdings · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British ColumbiaHEC Montréal
Fundersnot available
KeywordsPoliticsExploitRegression discontinuity designCompetition (biology)Code (set theory)Panel data

Abstract

fetched live from OpenAlex

Data and code accompanying "Money and Politics: The Effects of Campaign Spending Limits on Political Entry and Competition".<br><br>Abstract: This paper studies the effects of campaign spending limits on the political entry, selection, and behavior of local politicians in Brazil. We analyze a reform that limits campaign spending for mayoral elections. The limits were implemented with a discontinuity that we exploit for causal identification. We find that stricter limits reduce reelection rates and increase political competition by attracting more candidates who are also less wealthy and rely less on self-financing. Despite their effects on electoral outcomes, stricter limits did not lead to significant short-run improvements in policy outcomes, such as in education and health.<br><br>

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.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.008
Research integrity0.0000.001
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 head0.060
GPT teacher head0.330
Teacher spread0.270 · 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

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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