Sustainability within the Academic EcoHealth Literature: Existing Engagement and Future Prospects
Bibliographic record
Abstract
In September 2015, 193 Member States of the United Nations agreed on a new sustainable development agenda, which is outlined in the outcome document Transforming our world: the 2030 Agenda for Sustainable Development. EcoHealth is an emerging field of academic inquiry and practice that seeks to improve the health and well-being of people, animals, and ecosystems and is informed in part by the principle of sustainability. The purpose of this study is to investigate which sustainability terms and phrases were engaged in the academic EcoHealth literature, and whether the engagement was conceptual or non-conceptual. To fulfill the purpose, we searched four academic databases (EBSCO All, Scopus, Science Direct, and Web of Science) for the term “ecohealth” in the article title, article abstract, or in the title of the journal. Following the search, we generated descriptive quantitative and qualitative data on n = 647 academic EcoHealth articles. We discuss our findings through the document Transforming our world: the 2030 Agenda for Sustainable Development. Based on n = 647 articles, our findings suggest that although the academic EcoHealth literature mentions n = 162 sustainability discourse terms and phrases, the vast majority are mentioned in less than 1% of the articles and are not investigated in a conceptual way. We posit that the 2030 Agenda for Sustainable Development gives an opening to the EcoHealth scholars and practitioners to engage more with various sustainability discourses including the 2030 Agenda for Sustainable Development.
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 | 0.073 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.038 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.037 | 0.034 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".