SAGA: TRANSLANGUAGING AND SUSTAINABILITY: A Green Paper to Seed and Grow the Research Project
Bibliographic record
Abstract
SAGA is a 5 year research project (2023-2028) to investigate and advance sustainability transitions across language and context. The project's goal is to advance more-than-English language capacity as an overlooked means to embed diverse cultural values within more effective sustainability strategies.For some, sustainable development is an international signifier of a greener, fairer world. For others, it is an empty signifier. The fact that English is the lingua franca of sustainable development discourse and policy is one barrier to the emergence of a cultural code of sustainability that is needed for a sustainability transition. Removing this barrier requires more than rough translation; it demands adequate interpretation, contextualization, and connections to communities in place – a process of translanguaging.With reflexive, observational and collaborative investigations in English, French, Finnish, Danish, and Indigenous languages, in different urban contexts, the SAGA research team will investigate the translanguaging processes that permit and inhibit the activation of sustainable cities in ways that hold cultural meaning.We aim to crack the lived coding of sustainable cities, as opposed to their global blueprints, by inquiring into the role of language in sustainability talk and sustainability interventions in monolingual, bilingual and multilingual contexts.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".