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Record W4244485049 · doi:10.4095/301309

Age Structure, 2006 - Golden Years by Census Subdivision (65 - 79 years)

2010· report· en· W4244485049 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsCensusSubdivisionGeographyAge structureDemographyGenealogyArchaeologyHistoryPopulationSociology

Abstract

fetched live from OpenAlex

Canada is an aging society. In 2006, 13.7% of the total population of Canada was 65 years and over. This proportion was 9.7% only twenty five years ago in 1981. During the same period, the proportion of the population that was very old increased at a more rapid pace. For example, between 1981 and 2006 the proportion of the population that was 80 years and over rose from 1.7% to 3.7%. The number of people in this age group topped the 1 million mark (at 1.2 million) for the first time in 2006. In 2006, the population of Saskatchewan was the oldest in the country with 15.4% of the population 65 years and over. It also had the largest proportion of the oldest old, where one out of every 20 Saskatchewan residents was 80 years of age and over. The national average was one in 27. Saskatchewan's situation is unique, in that it has both the largest proportion of seniors and one of the largest proportions of children among the provinces. This is attributable to several factors: higher fertility compared to any other Canadian province due to a large Aboriginal population; a life expectancy that was, until quite recently, one of the highest in the country; and substantial losses of young adults migrating to Alberta to find employment. In general, Atlantic Canada (Newfoundland, and Labrador, Prince Edward Island, Nova Scotia, and New Brunswick) and British Columbia had an older age structure population (14-15% in the age group 65 and over) compared with the national average, once again a reflection of their lower fertility rates.

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.327
Teacher spread0.303 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2010
Admission routes1
Has abstractyes

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