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Record W4402644206 · doi:10.7202/1113556ar

Guerrilla Gardening in a Time of Ecological and Social Crisis: An Exploratory Endeavor to Feel Connected with a Lost Piece of Forest

2024· article· en· W4402644206 on OpenAlexvenueno aff
Margaretha Häggström, Mårten Häggström

Bibliographic record

VenueThe Trumpeter · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen journalismEcological crisisEcologyExploratory researchSociologyEnvironmental ethicsGeographyPolitical scienceSocial scienceBiologyLaw

Abstract

fetched live from OpenAlex

This four-year explorative study is located in a nearby forest, or more accurately a clearcut, in the western part of Sweden. The objective is to contribute to existing knowledge about forest gardening by exploring a forest milieu without trees in the Northern Hemisphere where the conditions are poor. The aim is to support biodiversity in the short term and to find out whether we could cultivate root vegetables, potatoes, and summer flowers in this nutrient-poor and shadowless environment. The study has an eco-philosophical approach that promotes eco-pedagogy and environmental education and a pragmatic pedagogical approach that adopts a "learning-by-doing" belief. The exploration is underpinned by collaborative autoethnography, and the research is conducted in and through practice. The findings show that a planting project in a clearcut requires a lot of preparation and planning and that it is possible to use a clearcut to grow potatoes, peas, and some summer flowers without special efforts, but hard to grow other crops such as vegetables and root vegetables. The project implies that the planting activities in a clearcut may have a deep impact on people’s sense of human-nature identity and respect for the natural environment.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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 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
Published2024
Admission routes1
Has abstractyes

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