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Record W1993632630 · doi:10.1177/002795011422700105

Can an Ageing Scotland Afford Independence?

2014· article· en· W1993632630 on OpenAlexaff
Katerina Lisenkova, Marcel Mérette

Bibliographic record

VenueNational Institute Economic Review · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of Ottawa
FundersEconomic and Social Research Council
KeywordsStatus quoIndependence (probability theory)Context (archaeology)Population ageingEconomicsComputable general equilibriumPopulationScope (computer science)Government (linguistics)Public economicsAgeingDemographic economicsDevelopment economicsGeographyMacroeconomicsDemographyMarket economySociologyBiology

Abstract

fetched live from OpenAlex

The aim and scope of this paper is to isolate the effects of population ageing in the context of potential Scottish independence. A dynamic multiregional Overlapping Generations Computable General Equilibrium (OLG-CGE) model is used to evaluate the two scenarios. The status quo scenario assumes that Scotland stays part of the UK and all government expenditures associated with its ageing population are funded on a UK-wide basis. In the independence scenario, Scotland and the rest of the UK pay for the growing demands of their ageing populations independently. The comparison suggests that Scotland is worse off in the case of independence. The effective labour income tax rate in the independence scenario has to increase further compared with the status quo scenario. The additional increase reaches its maximum in 2035 at 1.4 percentage points. The additional rise in the tax rate is non-negligible, but is much smaller than the population ageing effect (status quo scenario) which generates an increase of about 8.5 percentage points by 2060. The difference for government finances between the status quo and independence scenarios is thus relatively small.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.105
GPT teacher head0.468
Teacher spread0.363 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2014
Admission routes1
Has abstractyes

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