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Record W1537221861 · doi:10.1002/9781119945352.ch8

Social Security and Social Protection

2012· other· en· W1537221861 on OpenAlexaff
Felicity Callard, Norman Sartorius, Julio Arboleda‐Flórez, Peter Βartlett, Hanfried Helmchen, Heather Stuart, Jose Taborda, Graham Thornicroft

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsQueen's University
Fundersnot available
KeywordsSocial securityComputer securityInternet privacySocial protectionComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

A past focus on anti-discrimination legislation has tended to overshadow attempts to develop progressive social protection/social security legislation. There is growing interest about how to build up rights-based welfare and social protection legislation that departs from older, ‘charity’ or ‘compensation’ based models. Such rights-based legislation is regarded as increasingly indispensable in attempts to address the deep structural barriers (to employment, to adequate control over one's social and economic relationships) that people with mental health problems face. Social security and social protection legislation can, if designed and implemented well, offer a powerful social and economic tool for development. We emphasise the need for transparent regulatory mechanisms (to govern the relationship between government, service providers and service users) and appropriate rights-based standards and funding mechanisms. National disability policies can help support legislative approaches in attempting to ensure that social security measures for people with disabilities facilitate a reasonable quality of living.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.014
Scholarly communication0.0060.004
Open science0.0000.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0260.003

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 head0.116
GPT teacher head0.403
Teacher spread0.287 · 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 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

Citations1
Published2012
Admission routes1
Has abstractyes

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