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Record W4399771537 · doi:10.32920/26053093.v1

Opportunities and Barriers to the Delivery of Place-based Environmental Education During Emergency and Non-emergency Teaching: An Information and Communication Technology Approach

2024· preprint· en· W4399771537 on OpenAlexaff
Inga Borisenoka

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEmergency responseBusinessKnowledge managementMedical emergencyEnvironmental planningComputer scienceMedicineEnvironmental science

Abstract

fetched live from OpenAlex

<p>Place-based environmental education (PBEE) can enrich environmental education outcomes for youth through direct contact with nature. However, PBEE in K-12 can face barriers, some of which have been exacerbated during emergency teaching protocols, as observed during the COVID-19 pandemic. This research investigates K-12 teachers' perspectives on the potential of leveraging information and communication technology (ICT) to support PBEE during emergency and non-emergency teaching. Survey (n=122) and focus group (n=24) findings identify barriers and opportunities to PBEE before and after emergency protocols and how teachers view the use of ICT in PBEE. There was a negative association between PBEE frequency and grade level taught. The emergency protocols decreased time availability to plan for PBEE due to other work responsibilities. There were more references about potential positive aspects of ICT in PBEE than negative. Nevertheless, teachers emphasized that ICT use should be intentional and limited to allow students to engage thoroughly with the environment. </p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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