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Record W4200024321 · doi:10.52326/jss.utm.2021.4(4).01

UNDERSTANDING THE SOCIAL SCIENCES

2021· article· en· W4200024321 on OpenAlexaboutno aff
Titu-Marius I. Băjenescu

Bibliographic record

VenueJournal of Social Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyQuarter (Canadian coin)PopulationDemographySocial securityPopulation ageingInequalityDemographic economicsOld Age SecurityRetirement agePolitical scienceSociologyGerontologyBirth rateGeographyEconomicsMedicineFertilityPensionLaw

Abstract

fetched live from OpenAlex

Old age is difficult to define, so many terms overlap or clash with each other, all of which raise a number of issues: the elderly, the third age, the fourth age, senior citizens, pensioners, etc. It is not easy to determine the threshold for entering the period of life commonly known as old age. One thing is certain, old age has changed profoundly. From now on, it has become for everyone, albeit with profound inequalities, a normal stage of life. Social security systems combined with the considerable progress in medicine have made it possible to increase the length of retirement. Whereas in 1950 a man retiring at 65 could expect to live for about 12 years, today life expectancy at 60 is over 20 years for men and over 25 years for women. However, this simple observation has much more complex consequences in terms of the social identity, integration and social behaviour of these new population groups. Despite an ageing population, Switzerland has a total labour force of 4.706 million people. In the fourth quarter of 2019, the participation rate of the population aged 15 and over was 68.1%. This puts the country in second place in Europe behind Iceland (79.9%). Switzerland's neighbouring states have significantly lower levels (Germany: 62.6%, Austria: 61.4%; France: 55.5%, Italy: 49.9%). In particular, Switzerland has one of the highest rates of employed women in Europe. The percentage of employed women increased significantly between 2010 and 2019, from 56.9% to 60%.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0180.007
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.374
GPT teacher head0.467
Teacher spread0.094 · 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 designTheoretical or conceptual
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

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