Appropriate Curriculum: Enabling The Student To Meet The Transdisciplinary Challenges Of A Sustainable Society
Bibliographic record
Abstract
A sustainable society at present world population levels is faced with many complex issues: curbing further human population growth, preventing nuclear, biological or chemical wars, soothing social and political tensions, fighting poverty, protecting the environment from poison and climatic change, coping with resource scarcity, and managing vulnerable ecosystems. Each one of the items in this list transcends our conventional disciplines. Considering further that all of the problems are connected makes it obvious that neither a scientifically illiterate public nor our professionals, traditionally trained in narrow disciplines, are capable of creating or maintaining a sustainable society.Scientific and ordinary literacy of the general public is a desirable if not a necessary preparation for a sustainable society. Can it be achieved through our present educational means, or is it necessary for education to change? Today, the alphabet and grammar have become simple enough for all to learn how to read and write with a minor effort, and illiteracy in developed countries is now the exception rather than the rule. Unfortunately, this is not the case with scientific literacy. To learn science today is hard and time consuming. Our scientific and engineering knowledge is fragmented into many disciplines, and our curricula in these fields are cluttered with insignificant details. The frustrating information overload prevents most contemporaries from becoming scientifically literate, and it is difficult to get even the simplest of scientific truths to a wide public.A new knowledge structure for the development of a unified science curriculum is presented in this paper. By using universal concepts and universally applicable algorithms of thinking, a knowledge core is presented which connects all the disciplines and avoids duplication. It is concluded that such a unified science reduces the quantity of information required for a broad view of existing knowledge, that the reduced effort in learning such a universal mental tool will motivate more students to think scientifically about broad issues, and that the professionals trained in transdisciplinary sciences will be able to see the "big picture" of the problems facing a sustainable society.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".