Bibliographic record
Abstract
Over the last few decades, Sustainability Science (SS) has gained momentum and emerged in the academy with an extraordinary performance in knowledge production and contributions to the research and publications enterprise, growth in academic programs at the undergraduate and graduate levels, creation of centers/laboratories, and formation of scientific communities, networks and associations. Increasingly, terms like integration, collaboration and bridging of fields and disciplinary boundaries are in the forefront of conversations and/or debates. The key question addressed by this review essay is this: Is interdisciplinarity in Sustainability Science a challenge or opportunity for educational institutions and local communities in the 21st century? Considering the recent momentum in educational advancements and institutional progress, the study outlines relevant literature on interdisciplinarity in SS; synthesizes recent thinking and developments in SS and attempts to address what challenges and opportunities people across the globe face in sustainability education and research and in the development of academic programs and sustainable communities. When scientists, policy makers, academics get together as teams, partners and collaborators they are likely to be engaged in interdisciplinary work and possibly doing sustainability research, policy development and problem-solving to deal with the pressing demands and challenges of the 21st century society. In this context, it is urgent in science and society to seek solutions to major sustainability problems such as climate change and one way to address that is by doing interdisciplinary work in Sustainability Science.
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.
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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".