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Record W4220652968 · doi:10.1080/13504622.2022.2045905

Petting bees or building bee boxes? Strategies for transformative learning

2022· article· en· W4220652968 on OpenAlexaff
Jill Bueddefeld, Julie Ostrem, Michelle Murphy, Elizabeth Halpenny, Brian Orr

Bibliographic record

VenueEnvironmental Education Research · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsAlberta Environment and Protected AreasUniversity of AlbertaWilfrid Laurier University
Fundersnot available
KeywordsTransformative learningExperiential learningParticipatory action researchEnvironmental educationAction researchPedagogyPsychologyAction (physics)Science educationCitizen scienceCitizen journalismSociologyPolitical science

Abstract

fetched live from OpenAlex

This paper presents findings from a research study exploring the effectiveness of three environmental education programs. The first was an interpretive program where visitors learned to safely interact with and ‘pet’ bees (control group), the second was the same interpretive program with the addition of post-visit action resources (interactive treatment group), and the third was a contributory citizen science bee box building project (citizen science treatment group). Using personal meaning maps, interviews, and participatory observations we explored learning outcomes in relation to Transformative Learning Theory. This study found that the interpretive program facilitated a more complex learning experience across all transformative learning domains. The participants who engaged in a contributory citizen science bee box project demonstrated a narrower, albeit more focused learning experience where their knowledge increased specifically in relation to understanding the issues native bees face and actions related to native bee conservation. These findings have important implications for experiential learning where action outcomes and transformative learning are interrelated goals.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.000

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 head0.086
GPT teacher head0.362
Teacher spread0.277 · 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 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

Citations12
Published2022
Admission routes1
Has abstractyes

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