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Record W3048395577 · doi:10.26686/pq.v16i3.6557

Doubling Down on Children and Young People’s Aspirations Post-lockdown

2020· article· en· W3048395577 on OpenAlexaff
Donna Provoost, Katie Rose Esther Bruce

Bibliographic record

VenuePolicy Quarterly · 2020
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PsychologyDevelopmental psychology2019-20 coronavirus outbreakOrder (exchange)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineBusiness

Abstract

fetched live from OpenAlex

Children and young people experienced the Covid-19 lockdown differently from adults, and we need to consider these impacts as part of the recovery measures. Prior to Covid-19, cracks in our social system were already evident. At that time, children and young people told us what they wanted in order to create better future. The aspirations they shared are even more relevant post-Covid-19. Their views shaped the Child and Youth Wellbeing Strategy, and our best approach to supporting child wellbeing as part of the recovery is to double down on implementing this strategy. This will ensure that the recovery response is child-centred, holistic and aspirational.

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)0.006
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0060.006
Open science0.0010.016
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.028
GPT teacher head0.359
Teacher spread0.330 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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