A 15 C’s Pathway of Sustainability in Environmental Health Management & The Crucial Role of Higher Education Institutions
Bibliographic record
Abstract
Manmade environmental degradation has created an unsustainable status quo posing many known and yet unknown environmental health threats. Innovation, which may be considered as a function of time, location, and explicit and tacit knowledge acquisition, is essential for the effective and sustainable management of environmental health issues. The following series of C’s may function as a pathway towards sustainability in environmental health management: (1) Consciousness of the urgency, (2) Comprehension of the complexity, (3) Confidence in our ability to change, (4) Capacity-building for decision-making, (5) Cooperation among stakeholders, (6) Carbon footprint reduction, (7) Circular economy adoption, (8) Corporate sustainability, (9) Creativity (Creative thinking and action), (10) Creation of resilient and adaptive communities, (11) Creation of sustainable living environments for all social groups, (12) Creation of new explicit and tacit knowledge, (13) Communication of the new knowledge, (14) Curriculum updates, and (15) Crisis management. Apart from the emerging teaching and research priorities, the proposed pathway requires a strategic higher education institutions’ contribution to the necessary societal transformation towards sustainability. Higher education institutions could play a crucial role in all the described steps of this 15 C’s pathway and in the interconnections between them. Each step may offer emerging opportunities for innovative planning and action towards a more sustainable future. However, further research and pilot applications are necessary for the evaluation of the proposed theoretical model.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".