Experiments with a Dark Pedagogy: Learning from/through Temporality, Climate Change and Species Extinction (…and Ghosts)
Bibliographic record
Abstract
Abstract In this article, we experiment with a form of dark pedagogy, a pedagogy that confronts haunting pasts∼presents∼futures in environmental education. We offer a conceptualisation of ghosts that enables us to creatively explore the duration of things and consider the relationality of time. We examine this through two situated contexts, engaging with entangled, yet differentiated, socioecological issues. The first issue involves the cascading impacts of climate change on the Australian Alps, including intensifying bushfires and threats to the iconic snow gum. The second issue involves the reordering of human/animal relations through processes of settler colonialism that continue to transform land into a commodity, with a significant cultural and material consequence of such colonial harm resulting in the extermination of free-ranging bison herds in the Canadian prairies. Both are unique issues, but both involve impacts of colonisation, loss and natural-cultural hegemony. The dark elements of these Place-specific stories involve noticing and confronting loss and related injustices. In our case, we diffract such confrontations by thinking through these challenging issues and working towards ethical ways of living and learning. In this article, we (re)member ghosts and ponder practices for fostering anticolonial response-abilities and affirmative human/Earth futures.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".