MétaCan
Menu
← Back to cohort
Record W6968954236 · doi:10.5281/zenodo.5158128

LITERACY, SENSITIVITY AND OPPORTUNITIES IN DEVELOPING ENVIRONMENTALLY RESPONSIVE BEHAVIOR AMONG GRADE 7 LEARNERS

2021· article· en· W6968954236 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyQuarter (Canadian coin)Environmental educationDispositionEnvironmentally friendlyKnowledge level

Abstract

fetched live from OpenAlex

The study sought to describe the environmental literacy and environmentally responsible behavior of Grade 7 students of Sto. Tomas Integrated High School. Furthermore, it attempted to know if there is a significant relationship between environmentally responsible behavior and environmental literacy as to context, competencies, knowledge and disposition. Using a descriptive correlation design, the study involved 150 Grade 7 students of Sto. Tomas Integrated High School who were randomly selected. The study was administered during the third quarter of the school year 2020-2021. The student-respondents were asked to answer survey questionnaire to determine the level of the Environmental Literacy and the extent of practice of Environmentally Responsible Behavior. Survey questionnaires were validated by the panel of examiners and group of teachers. Results revealed that there was a significant relationship between environmentally responsible behavior and environmental literacy as to context, competency and disposition while no significant relationship was obtained between environmentally responsible behavior and environmental literacy as to knowledge.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.261
Teacher spread0.225 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicEnvironmental Education and Sustainability→French-language works237,207→