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Record W2362877187

The Effect of Instruction on Children’s Knowledge of Marine Ecology, Attitudes Toward the Ocean, and Stances Toward Marine Resource Issues

2000· article· en· W2362877187 on OpenAlexaffvenue
Shirley Cummins, Gloria Snively

Bibliographic record

VenueCanadian journal of environmental education · 2000
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsResource (disambiguation)Environmental educationUnit (ring theory)TRIPS architectureExperiential learningEcologyPsychologyPedagogyMathematics educationEngineeringBiologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This study examined grade 4 students’ knowledge of marine ecology, their attitude towards the seashore and ocean, and their stances (preservationist, conservationist, exploitive) towards marine resource issues before and after a classroom instructional unit. Students’ pre-instructional and post-instructional questionnaires, drawings and writing were collected and analyzed. Prior to instruction, the students’ knowledge level was low, however their ocean attitudes were positive and they were predominately preservationist and conservationist in their stances. The instructional unit was developed using a constructivist perspective with an emphasis on experiential learning and included field trips to the seashore and the investigation of a local marine resource issue (Pacific salmon stock depletion). After instruction, a significant increase in knowledge and positive attitude was evident. As well, students’ stances toward marine resource issues were less polarized. The results have implications for environmental education in general and marine ecology and resource management studies in particular.

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)0.001
metaresearch head score (Gemma)0.012
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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

Citations49
Published2000
Admission routes2
Has abstractyes

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Same venueCanadian journal of environmental educationSame topicEducational Research and PedagogyFrench-language works237,207