The Noetic Propaedeutic Pedagogy as a Panacea to Environmental Degradation
Bibliographic record
Abstract
The phrase, ‘sustainable development’ has in recent times become a household phrase, thanks to the works and agitations of the environmental philosophers, scientists, animal right activists and other concerned bodies and groups. However, in spite of these campaigns, environmental degradation still goes on apace. This shows that the popularity of the phrase ‘sustainable development’ has remained only in theory and has not been translated to actions. The researchers believe that the problem of environmental degradation persists because of the divisive and polarising mindset with which man operates with. The moment this divisive mindset is converted to egalitarian one, humans would begin to see the environment as an important aspect of their life, without which their existence would be negated. An egalitarian mindset sees other beings as missing links sharing the same complementary framework of existence. Because they share the same framework, negation of the other becomes negation of the self. Negation of the environment is negation of the self. This kind of mindset according to Asouzu, could be attained through the process of noetic propaedeutic pedagogy. This is a kind of education of the mind that is geared towards converting the mind from its divisiveness to egalitarianism. This work is therefore, aimed at showcasng the noetic propaedeutic pedagogy as a profitable way to tackling the environmental crisis.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".