Relational Ontologies and Multispecies Worlds: Transdisciplinary Possibilities for Environmental Education
Bibliographic record
Abstract
OverviewIn this Editorial, we provide an overview of the Special Issue on Relational Ontologies and Multispecies Worlds: Transdisciplinary Possibilities for Environmental Education.We set out to weave together insights from the sixteen contributions with our considerations, as Guest Editors, and literature that has informed this project.Elaborating on some of the problems that authors seek to address, we then present what we believe to be the purpose and significance of this collection of work.The purpose is a performative attempt to address some of the wicked problems implicating a flourishing planet and empowering modes of (environmental) education that purport social and ecological justice.The significance is the critical and creative approaches to curricular and pedagogical enactments that each author adopts through the combination of rich theoretical and empirical inquiries for (re)configuring different temporal horizons.We then articulate what we mean by relational ontologies and multispecies worlds, and transdisciplinary environmental education.Providing a meta-analysis of each article, we then position author contributions within four interrelated and intersecting clusters: Wrestling in the ruins: The hope for decolonial environmental education, Kinning in the ruins: The hope for creatures, critters and vegetal life, Playing in the ruins: The hope for productive technologies, Belonging in the ruins: The hope for relational care ethics.Filled with hope and inspiration from each author's insightful contributions to this Special Issue, we then query what might come next in the quest to imagine and actualise relational and sustainable presents and futurities.What is the problem?Widespread climate disruptions and ecological and social destruction, degradation and fragmentation across the planet are intensifying, causing problems for all life on earth.It only takes a cursory glance at the news (or out your window!) to see the latest unprecedented disturbance.The site-specific details of environmental damage can easily be forgotten when they happen at a distance and are talked about at a planetary level.However, as you will read within the pages of this Special Issue, the specific locations some of us live and work are increasingly impacted, with relational and sustainable solidarities between (Western) people and with the Earth needed now more than ever.As such, we have invited authors to highlight both the crux of
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".