A<i><scp>Y</scp>in‐<scp>Y</scp>ang</i>approach to education policy regarding health and the environment: early‐careerists' image of the future and priority programmes
Bibliographic record
Abstract
Abstract Since the inception of sustainable development (SD), there has been a somewhat ignored contradiction between paradigms that are ecosystem‐based and paradigms that are human‐based or purely economic. We suggest that this contradiction can be unified through a balance of the two. TheChineseYin‐Yang philosophy is applied as a tool or approach to seeking balance between these ecocentric and anthropocentric paradigms. Priority education policy design for the merging of ecology and health are projected through an Ecohealth lens in response to increasingSDchallenges and the intention of the international Ecohealth organization to contribute toSDgoals. MeetingSDgoals along the nexus of health and environment is further considered through early‐careerist cultural assessments and projections. The groups considered for their professional image of the future are: members of the Ecohealth Association Student Section andChinese early‐careerists participating in a related conference. In response toSDgoals, a problem‐based learning design is suggested as an education policy priority. Rather than approachingSDas a boolean concept, for example, by either focusing on ecosystem sustainability or economic development, we suggest education policy for programmes and curriculums that will help emerging professionals balance these paradigms, so as to best address national and global challenges.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".