Examining the Scholarly Literature: A Bibliometric Study of Journal Articles Related to Sustainability and the Arts
Bibliographic record
Abstract
The Arts shows great promise in working toward a sustainable future as they can have a significant influence on the development of cultural norms. Using bibliometrics, this study uncovers the current body of scholarly literature related to the intersection of sustainability and the Arts. The results show that while there are very few articles (n = 77) published in scholarly journals related to this area, the number of manuscripts and the number of journals publishing manuscripts related to this subject area is increasing. Further, while there is no one individual who stands out to date as a leader in this field, the results show that Australia and Canada have produced the most published articles. Finally, this study demonstrates that scholarly articles related to the Arts and sustainability are mostly being published in well-established interdisciplinary sustainability-related journals and journals associated with the field of education for sustainable development. The results of this study give a more definitive answer to the question: what scholarly literature resources currently exist on the intersection of the Arts and sustainability and offers the scholarly community a better idea of what and how those involved in this area are publishing and mobilizing knowledge regarding their work.
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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.178 | 0.227 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".