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Record W1929607783 · doi:10.25336/p6c89k

Ageing Populations in Post-Industrial Democracies: Comparative Studies of Policies and Politics

2014· article· en· W1929607783 on OpenAlexaffvenue
Michel Grignon, Byron G. Spencer

Bibliographic record

VenueCanadian Studies in Population · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPoliticsAgeingPopulation ageingPolitical scienceDevelopment economicsEconomic geographyEconomicsPolitical economySociologyPopulationDemographyBiology

Abstract

fetched live from OpenAlex

While the consequences of population ageing in developed and democratic nations have been considered by scholars from many disciplines, the editors note that "political science as a discipline has lagged behind in developing an integrated body of knowledge to answer the question of which generations get what, when, and how," and state that this volume "aims to be a building block for such a body of research" (p. 1).The two main research questions explored throughout the volume are: (a) Can it be said that generational warfare has replaced class warfare in industrialized countries?and (b) Are ageing democratic societies driven by the growing electoral clout of the elderly (the elderly power hypothesis) or rather by cost-containment policies targeted toward the elderly (so-called leakage hypothesis)?The volume brings together a collection of eleven studies from twelve scholars in six countries, and provides a mix of quantitative and qualitative studies, each with an "explicitly comparative-political lens" (p.9), intended to explore how political systems function as major generational changes take place in the context of fiscal restraint.The studies are diverse in the range of approaches that are used (some following a rational public choice approach, other relying more on path dependence and institutionalist perspectives) and, we find, insightful in the conclusions that are reached.The editors do a nice job of "mapping the field" in their introductory chapter, but an important quibble relates to their (widely shared) use of 65 and older as the marker of old when most of their comparisons extend extend over six decades; given that they emphasize the observed increases in life expectancy (including healthy life expectancy), it seems odd to continue to fixate on age 65.Indeed, the worsening of the dependency ratio would be largely offset if the concept of old were adjusted to reflect such gains; that point is noted (p.8) but then ignored.In any event, population projections do not play a major role in the chapters that follow.There are a few unfortunate editorial lapses: one is the occasional careless use of numbers, with a notable example appearing in Table 1.1, bottom panel, which reports, "Elderly care costs as a share of GDP" in 1980 and 2005.For the OECD-30 it is reported as 0.58 in 2005, meaning that a preposterous 58 per cent of GDP was used in this way, even though the text interprets it as "0.58 per cent of GDP" (p.8); furthermore, there is no description of the concept that is measured, and the source note is not sufficiently informative to enable readers to investigate.The long-run decline in the labour force participation rate of older male workers is noted, together

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.381
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.337
GPT teacher head0.468
Teacher spread0.131 · 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 teacher head, 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

Citations6
Published2014
Admission routes2
Has abstractyes

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