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Record W4414833453 · doi:10.1080/03323315.2025.2561633

Writing plants out of secondary biology education in a time of ecological crisis – staying with or avoiding the trouble?

2025· article· en· W4414833453 on OpenAlexafffund
Natalie O’Neill, Karen Kerr

Bibliographic record

VenueIrish Educational Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsQueen's University
FundersQueen's UniversityQueen's University Belfast
KeywordsSecondary educationEcological crisisEnvironmental educationCrisis response

Abstract

fetched live from OpenAlex

Research conducted across the UK links the fall in botanical expertise, with a decline in third-level botanical programmes of study, and a rise in ‘plant blindness’ (Stroud et al. 2022). Erosion of botanical education occurs at a time when national STEM policies are preoccupied with forging links to industry and commercialisation. These economic policies have failed to deliver on the intentions therein (Lynch 2022) and have resulted in us living on a damaged planet (Tsing et al. 2017) This study adopts Haraway’s theoretical framework (2016) to reimagine how we learn about the earth by forging new patterns of thought that deliberately entangle our lives with those of all species who bear the consequences of human actions. We critique the neoliberal constructivist discourse that promotes knowledge driven by economic demands (Carter 2005). Using the methodological approach of Bartlett and Vavrus (2017), we undertake a comparative case study, to map the erosion of botanical education from the biology curriculum in two countries – Ireland and Northern Ireland. This paper argues for biology education that will enable us to learn to live with other species on our damaged planet by shifting the focus of biology education to an eco-centered approach (Braidotti 2021).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.547

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 studies0.0000.000
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 head0.077
GPT teacher head0.409
Teacher spread0.332 · 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
Published2025
Admission routes2
Has abstractyes

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