MétaCan
Menu
Back to cohort
Record W4297852801 · doi:10.1080/13504622.2022.2120185

How do students at the end of secondary school consider the challenges of sustainable development of the Seine in France? What avenues for education?

2022· article· en· W4297852801 on OpenAlexaff
Agnieszka Jeziorski, Marco Barroca-Paccard, Faouzia Kalali

Bibliographic record

VenueEnvironmental Education Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsTransformative learningEnvironmental educationAgency (philosophy)Context (archaeology)Education for sustainable developmentSociologySustainable developmentPedagogySet (abstract data type)Outdoor educationPolitical scienceSocial scienceGeographyComputer science

Abstract

fetched live from OpenAlex

This article aims to understand how students aged 14–17 from different contexts in France consider the issues related to the Seine. 20 interviews were carried out focusing on the implementation of an educational approach enabling students to grasp the priority issues associated with the sustainable development of the Seine. The analysis brings out the way in which the students have experienced the educational approach; it aims to characterise their relationship with the river, and to define their eco-citizen engagement. Several specificities emerge depending on the context studied, but the students of the three schools underline the interest of approaches that go beyond the traditional education form and that are rooted in their local reality. Our article set the interest in valorising another way of learning where students feel more free and autonomous and also requires training teachers in the development of their agency for a transformative-sociocritical approach of ESE.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0080.003
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.335
Teacher spread0.315 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Education ResearchSame topicEnvironmental Education and SustainabilityFrench-language works237,207