Mathematics education for sustainable futures: a strengths-based survey of the field to invite further research action
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this survey, we introduce and prompt an article collection—"Mathematics Education for Sustainable Futures"—which we are guest editing. The collection will comprise original research articles, written and published over a 2-year period. Recognising the collection title as making reference to what is an emerging area of research, in this introduction, we survey work that has been done. In the spirit of opening and inviting new questions and research directions, we focus on what it means to take mathematics education for sustainable futures seriously, for how we practise and imagine mathematics education, including established topics such as curriculum, knowledge, pedagogy, teacher education, language, modelling, and technology. We structure our review around eight invitations (here arranged into six sections) in the call for collection articles. Each section ends with further invitations for potential authors, or others, wanting to locate and chart their own work in the space. We end drawing out two themes that resonate across the invitations: attention to who and what is marginalised and the importance of a deep reflexivity in our choice/use of concepts. This text is part of the article collection entitled “Mathematics Education for Sustainable Futures” (available at https://link.springer.com/collections/acebaagbha ).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it