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Record W2133852907 · doi:10.1177/0270467602238889

Development of Environmental Knowledge and Attitudes in Engineering Students

2002· article· en· W2133852907 on OpenAlexaff
Christopher Kennedy, R. A. Hyde, Bryan Karney

Bibliographic record

VenueBulletin of Science Technology & Society · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental educationTest (biology)Work (physics)PsychologyEnvironmental pollutionEngineering educationMedical educationMathematics educationPedagogyEngineeringEnvironmental protectionMedicineGeographyEngineering management

Abstract

fetched live from OpenAlex

A test was administered to 102 engineering students to ascertain how engineering education influences their environmental knowledge and attitudes. Answers to definitional and factual questions in a forced-answer section demonstrated that students were improving their technical knowledge, but responses to more subtle questions were mixed. Answers to attitudinal questions exhibited a trend towards increased environmental awareness. For open-ended questions, posttest results showed an increase in knowledge of engineering work. Over 80% of the students considered themselves to have a caring attitude toward the environment, with the “three R’s” and green transportation choices most commonly cited. Engagement in research, education, or advocacy doubled from pretest to posttest. Air pollution and solid waste disposal most frequently influenced students’ attitudes toward the environment. Outdoor experiences were the most frequently mentioned source of information; university courses rose from 4% to 15% on the posttest. Only 40% of the students could name an environmental role model.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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 teacher head, not a consensus.

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

Citations9
Published2002
Admission routes1
Has abstractyes

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