A “Strong” Approach to Sustainability Literacy: Embodied Ecology and Media
Bibliographic record
Abstract
This article outlines a “strong” theoretical approach to sustainability literacy, building on an earlier definition of strong and weak environmental literacy (Stables and Bishop 2001). The argument builds upon a specific semiotic approach to educational philosophy (sometimes called edusemiotics), to which these authors have been contributing. Here, we highlight how a view of learning that centers on embodied and multimodal communication invites bridging biosemiotics with critical media literacy, in pursuit of a strong, integrated sustainability literacy. The need for such a construal of literacy can be observed in recent scholarship on embodied cognition, education, media and bio/eco-semiotics. By (1) construing the environment as semiosic (Umwelt), and (2) replacing the notion of text with model, we develop a theory of literacy that understands learning as embodied/environmental in/across any mediality. As such, digital and multimedia learning are deemed to rest on environmental and embodied affordances. The notions of semiotic resources and affordances are also defined from these perspectives. We propose that a biosemiotics-informed approach to literacy, connecting both eco- and critical-media literacy, accompanies a much broader scope of meaning-making than has been the case in literacy studies so far.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".