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Record W2951613157 · doi:10.1017/9781108292337

Incentives to Pander

2018· book· en· W2951613157 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCambridge University Press eBooks · 2018
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsIncentivePoliticsRevenueEconomic policyExciseInvestment (military)Public economicsEconomicsTax revenueTax incentiveBusinessFinanceMarket economyPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

Policies targeting individual companies for economic development incentives, such as tax holidays and abatements, are generally seen as inefficient, economically costly, and distortionary. Despite this evidence, politicians still choose to use these policies to claim credit for attracting investment. Thus, while fiscal incentives are economically inefficient, they pose an effective pandering strategy for politicians. Using original surveys of voters in the United States, Canada and the United Kingdom, as well as data on incentive use by politicians in the US, Vietnam and Russia, this book provides compelling evidence for the use of fiscal incentives for political gain and shows how such pandering appears to be associated with growing economic inequality. As national and subnational governments surrender valuable tax revenue to attract businesses in the vain hope of long-term economic growth, they are left with fiscal shortfalls that have been filled through regressive sales taxes, police fines and penalties, and cuts to public education.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.037
GPT teacher head0.192
Teacher spread0.155 · 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