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Record W3125672505 · doi:10.1086/508248

Academic Earmarks and the Returns to Lobbying

2006· article· en· W3125672505 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

VenueThe Journal of Law and Economics · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRepresentation (politics)Work (physics)EconomicsInstrumental variableAccountingEconometricsPolitical scienceLawPoliticsEngineering

Abstract

fetched live from OpenAlex

In this paper, we estimate the returns to lobbying by universities. To motivate our empirical work, we develop a simple theoretical model of university lobbying for academic earmarks. Our statistical analysis shows that universities represented by a House Appropriations Committee (HAC) or Senate Appropriations Committee (SAC) member spend less money on lobbying than those that are not represented. In addition, using instrumental variables estimations, we show that universities without HAC or SAC representation may receive some benefit to lobbying for earmarks, although in many estimations this benefit is not statistically different from zero. However, for universities with HAC or SAC representation, a 10 percent increase in lobbying yields an additional 2.8 percent or 3.5 percent increase in earmarks, respectively. This suggests that there are large returns to lobbying for academic earmarks if a university is represented by a member of one the HAC or SAC, but little or no return if not.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.182

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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