MétaCan
Menu
← Back to cohort
Record W2770029740

Resiliens: Kært barn har mange navne

2017· article· da· W2770029740 on OpenAlexaboutno aff
Niels Leonhard Rebsdorf, Celine Rigmor Louise Ferot

Bibliographic record

Venuenot available
Typearticle
Languageda
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMangeBarnBarn-owlGeographyBiologyEcologyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Resiliensbegrebet er kommet til Danmark. Hvordan er begrebet interessant i en dansk skolekontekst? Resiliens har i mange år været et centralt begreb for skolen og dens samarbejdspartnere i Canada, England og Australien. Det bruges i forbindelse med børns karakterdannelse og skolens og lokalsamfundets fællesskabsopbygning. Resiliens rummer store muligheder for at give indhold, sprog og værktøjer til at arbejde med dannelse og karakterdannelse i skolen. Der knytter sig en række åbenlyse muligheder, men også udfordringer til begrebet i en dansk skolekontekst, og det vil vi undersøge i denne artikel.

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)0.006
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.054
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0070.005
Open science0.0010.009
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0540.016

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 head0.066
GPT teacher head0.464
Teacher spread0.399 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicResilience and Mental Health→French-language works237,207→