Translanguaging for more-than-English sustainability transitions
Bibliographic record
Abstract
Critical sustainability research pays insufficient attention to the need for language justice in action toward sustainable cities. Addressing this gap can increase inclusive action competence in sustainability. Including diverse languages, along with the work that goes into communicating effectively across languages, can counter the disproportionate dominance of English in sustainability. This is an underexamined aspect of the sustainability knowledge-action gap. Language diversity is large and growing in cities and professions pursuing sustainable development. While English is the lingua franca of sustainability science, learners and actors engage in translanguaging or moving across languages to inform their understanding and communication in culturally and personally relevant ways. Recognising the effort that goes into translanguaging and highlighting language-specific entry points to sustainability transition can deepen the meaningfulness of local sustainability efforts. We designed a workshop to engage the translanguaging competencies of interdisciplinary sustainability learners. Results demonstrate that students were able to build shared understanding through different languages and relate the concepts across languages. This was inclusive of cultural values and personally rewarding for participants. The workshop additionally exposed students to sustainability terms in a language foreign to all of them, Finnish. Students were able to relate meanings to a spectrum of sustainability-related actions that were more readily accessible to them than conventional English language action agendas for sustainability. This workshop demonstrates a replicable methodology to increase attention to diverse languages for activating sustainability agendas. The process of translanguaging, moving across languages to communicate and convey meaning, holds unrecognised value for inclusive sustainability action competence.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".