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Record W2891487436 · doi:10.5281/zenodo.1240342

Partispace Deliverable 7.2: Youth Participation Training Module

2018· article· en· W2891487436 on OpenAlexaff
Barry Percy‐Smith, Nigel Thomas, Zulmir Bečević, Ilaria Pitti

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsEducation and Early Childhood Development
FundersEuropean Commission
KeywordsDeliverableTraining (meteorology)Computer sciencePublic relationsMedical educationPolitical scienceEngineeringGeographyMedicineSystems engineeringMeteorology

Abstract

fetched live from OpenAlex

This module has been developed from the Partispace research on spaces and styles of youth participation, conducted in eight European cities between 2015 and 2018. The research was undertaken by a team led by Andreas Walther of Goethe University Frankfurt and funded by the European Union under the Horizon 2020 programme. The aim of the module is to use key findings from this ground-breaking project to support learning and development amongst youth workers and other practitioners working with young people, as well as students of youth policy and practice. In the research, we learned about the interaction between policy and practice at the local, national and European levels. We learned about the settings in which young people participate and the purposes of that participation. We learned about the kinds of young people who participate and the rich variety of ways in which they participate. We learned about how young people, and those working with them, understand participation, and how much that is different from the dominant ‘official’ understandings. Above all, we learned about young people’s experiences of participation, and how those can be made better.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.006

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 head0.094
GPT teacher head0.257
Teacher spread0.163 · 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 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
Published2018
Admission routes1
Has abstractyes

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