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Record W2917371245 · doi:10.5539/hes.v9n2p22

Knowledge, Attitudes and Behaviours Concerning Sustainable Development: A Study among Prospective Elementary Teachers

2019· article· en· W2917371245 on OpenAlexfundvenueno aff
Francisco Borges

Bibliographic record

VenueHigher Education Studies · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
FundersUniversidade do MinhoInternational Council for Canadian Studies
KeywordsPortuguesePsychologyData collectionSustainable developmentKnowledge levelFocus groupMedical educationHigher educationMathematics educationSociologySocial scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

The aim of this study consisted in assessing knowledge, attitudes and behaviours concerning various aspects of sustainable development in a group of Portuguese university students and measure the influence of area of study for admission to higher education on this dimensions. The collection of data was undertaken via the completion of a questionnaire, which was designed to include the following dimensions: knowledge, attitudes and behaviours. This initiative took place in the 2016/2017 academic year and the focus/target group for was constituted by 168 prospective elementary teachers. The validation procedures of the questionnaire confirmed its three-dimensional structure. The results obtained showed the existence of very favourable knowledge and attitudes regarding sustainable development. Behaviours proved less favourable than the other two dimensions. In addition, the results show that respondents’ area of study for admission to higher education has no influence regarding knowledge, attitudes and behaviours concerning sustainable development. Finally some implications for teachers and students are raised and discussed.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.322
Teacher spread0.305 · 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 designObservational
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

Citations37
Published2019
Admission routes2
Has abstractyes

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