Bibliographic record
Abstract
As much as history education is supposed to be about the past, it is oriented towards the future. History teachers are guided by a variety of purposes, such as cultural inheritance, critical and disciplinary thinking, identity formation and personal development, or activism and social change. Each of these purposes is imbued with particular notions of memory, citizenship and other values relevant to preparing young people for the future. While it may not always be explicit, a prevailing assumption in history education, as with Canadian curriculum, generally speaking, is that the future is a place and time to which we should look forward, as it will improve upon the past. But as we are coming to know, that may not be a responsible or accurate frame to pass on to the next generation. What theoretical and practical supports can help history educators renew their teaching in light of the Anthropocene, and particularly the climate crisis? In seeking to attune history education to a relational, ecological and ethical future orientation, we turned to the fields of Indigenous studies, environmental history and climate change education. We suggest some new, and even radical, directions we might look as a community of history educators. In doing so, we hope to nurture solidarity in navigating uncertainty together. With a set of common questions, assumptions and goals to guide us, we may find ways of teaching and learning that respond more meaningfully to the precarity of our times.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".