Bibliographic record
Abstract
The United Nations’ Intergovernmental Panel on Climate Change (IPCC) recently stated that along with ecological threats such as massive-scale extinction, loss of freshwater marine ecosystems, and drastic ocean acidification, “[a]ll aspects of food security are potentially affected by climate change” (IPCC 18). Storms, droughts, and sea levels aside, continuing under the “business as usual” paradigm of carbon output and environmental waste means that a rapidly increasing percentage of the human population may die of starvation. Faced with the task of “educating ‘leaders for the future’” (Cotton et al., 2015, p. 456), it is critical that educators foster active engagement with such climate-change related issues. Following Cotton et al.’s (2015) claim that “developing students’ energy literacy is a key part of the ‘greening’ agenda” (p. 456), this workshop will focus on cultivating environmental literacy in English pedagogy at the post-secondary level. While there is ample research to support the general importance of environmental literacy (EL), there are few substantive outlines for implementing this material in the English classroom. This workshop offers English instructors hands-on assignments, exercises, and teaching strategies to help them cultivate EL as part of their pedagogy. In addition to practical pedagogical suggestions made throughout this article, three appendices offer detailed descriptions of particular classroom exercises.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".