MétaCan
Menu
Back to cohort
Record W4367299086 · doi:10.1017/s0143814x23000065

Government spending preferences over the life cycle

2023· article· en· W4367299086 on OpenAlexaffabout
Florence Vallée‐Dubois

Bibliographic record

VenueJournal of Public Policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsStatus quoGovernment spendingGovernment (linguistics)Public spendingPublic supportEconomicsPublic economicsPublic policyDemographic economicsEconomic growthPolitical scienceWelfareMarket economy

Abstract

fetched live from OpenAlex

Abstract Do seniors have different public spending preferences than younger people? The literature on this topic has been limited so far to a few policies or to short periods of time, which makes it difficult to provide a comprehensive answer to this question. Using data from Canadian surveys conducted between 1987 and 2019 and covering fifteen policies, this paper shows that seniors, as compared to younger adults, are slightly more favourable to the status quo when it comes to government spending. Results also show that support for education spending decreases extensively over the life cycle, while support for environment spending decreases until middle age, then stabilizes. In contrast, support for transportation spending is more widespread in older age, while support for elderly services takes an inverted U-shape over the life cycle. These findings broaden our understanding of the influence of age on government spending preferences and allow us to reflect on the consequences of a growing senior electorate on government budgets.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.150
GPT teacher head0.488
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Public PolicySame topicGlobal Health Care IssuesFrench-language works237,207