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Record W218371791 · doi:10.4471/rasp.2014.04

Poverty Intervention in Relation to the Older Population in a Time of Economic Crisis: The Portuguese Case

2014· article· en· W218371791 on OpenAlexaff
InÃas Gomes, Maria Irene Carvalho, Isabella Paoletti

Bibliographic record

VenueResearch on Ageing and Social Policy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsCanadian Linguistic Association
Fundersnot available
KeywordsPovertyMainstreamEconomic growthPsychological interventionPopulationPoliticsDevelopment economicsSocial policyPolitical scienceIntervention (counseling)Public policyRhetoricEconomicsMedicineEnvironmental health

Abstract

fetched live from OpenAlex

In times of economic crisis, the distribution and impact of its effects vary greatly among social groups, due to the different level of exposure and availability of resources. This article conducts a policy analysis of the most important public policies and programmes fighting elderly poverty in Portugal, in the last two decades.It critically analyses the actual social and political situation, from three main perspectives: poverty approach; gender mainstream and public-private partnerships. The latest restriction measures have been jeopardizing the fight against poverty conducted in the last 15 years. Although poverty among the elderly is presently considered a political priority, no comprehensive policies are being developed. The policy interventions are directed towards extreme situations of poverty and dependency. Preventive measures are excluded from policies planning. The state is increasingly delegating to the social sector the social care responsibilities. Gender mainstream is still a rhetoric concept.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.119
GPT teacher head0.484
Teacher spread0.365 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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