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Record W7132967560

Transformative learning and informal environmental education: the case of community gardens

2004· dissertation· W7132967560 on OpenAlexaboutno aff
Martha Barriga Daunas

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningEnvironmental educationRaising (metalworking)AgricultureConsciousness raisingEnvironmental adult educationCommunity educationOutdoor education
DOInot available

Abstract

fetched live from OpenAlex

Gardeners who developed or coordinated a community garden had a higher level of participation, were more knowledgeable about environmental issues and had stronger beliefs in the impact of their actions than gardeners who participated as members. Educational programs can play an important role in raising awareness, fostering social and environmental change, encouraging leadership skills and nurturing transformative learning.Community gardening has great potential to transform participants' views of environmental, social and economic systems. This study explored the learning experiences of adult gardeners in Toronto. Some of the most significant learning outcomes were recognizing the worth of others and questioning assumptions of the dominant worldview. As participants reflected on their relationship with others, they develop more inclusive attitudes and practices. In some cases, the practice of organic agriculture helped gardeners to become more critical of conventional agriculture and to establish a closer connection with nature.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.013
Scholarly communication0.0040.002
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.254
Teacher spread0.244 · 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 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
Published2004
Admission routes1
Has abstractyes

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