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Record W2735241024 · doi:10.24193/ojmne.2017.22.01

The European Pillar of Social Rights: Adding Value to the Social Europe?

2017· article· en· W2735241024 on OpenAlexaff
Diana-Gabriela Reianu, Adela P. Nistor

Bibliographic record

VenueOn-line Journal Modelling the New Europe · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsConvergence (economics)PillarEuropean unionDivergence (linguistics)European commissionPolitical scienceValue (mathematics)Order (exchange)Social rightsDevelopment economicsEconomicsEconomic growthHuman rightsInternational tradeEngineeringLawComputer science

Abstract

fetched live from OpenAlex

The paper analyses the European Commission's latest major initiative in the social field, the European Pillar of Social Rights, examining the rationale behind this project, the merits and shortcomings of the mentioned proposal.Declared as an initiative that tries to overcome the negative effects of the crisis on the labour markets and social welfare systems, to heal the social wounds of Europe, and to renew convergence within the Euro area, this paper analyses the proposal through the lens of the major challenges that Europe is confronting nowadays, the convergence and divergence trends that we experiment inside the Union.Hence, the paper deals with the following questions: Does this initiative respond to the needs and challenges that Europe is facing today?; Does this initiative envisage policy avenues that encourage social convergence, that are capable of making a decisive impact on poverty, in order to reverse the threat of disintegration that faces the EU today?

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)0.008
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.021
Scholarly communication0.0140.013
Open science0.0010.008
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.093
GPT teacher head0.367
Teacher spread0.274 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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