MétaCan
Menu
Back to cohort
Record W3197476408 · doi:10.1007/978-3-030-73065-9_13

Intergenerational Controversy and Cultural Clashes: Political Consequences of Demographic Change in the US and Canada Since 1990

2021· book-chapter· en· W3197476408 on OpenAlexaboutno aff
Jennifer D. Sciubba

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsDemographic changePoliticsRhetoricSalience (neuroscience)PopulationPolitical scienceDevelopment economicsPolitical economyFederalistDemographic economicsEconomic growthSociologyDemographyEconomicsLaw

Abstract

fetched live from OpenAlex

This chapter offers a general introduction to demographic trends and projections in the US and Canada between 1990 and 2040 and discusses the economic fortunes of various groups, their mobilization capacity, influence of institutions and a rhetoric of population change. While the demographics of the US and Canada have undergone similar changes, including population ageing and shifts towards non-White national origin and race, their political dynamics are quite different. In particular, the mobilization capacity of US minorities is hampered by voter eligibility rules, an issue less relevant in Canada. In both, federalist institutions devolve power to state or provincial levels, but when combined with demographics yield different political impacts in the two countries. Finally, the tenor of rhetoric surrounding demographics has been nearly opposite in the two countries, although demographics are important in both. The chapter concludes with a reflection on the continued salience of population issues in the political arena in the coming decades.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.100
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0200.011
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.047
GPT teacher head0.310
Teacher spread0.263 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicSocial Policy and Reform StudiesFrench-language works237,207