Writing plants out of secondary biology education in a time of ecological crisis – staying with or avoiding the trouble?
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".