Creating Autonomy in the Advance of Teacher and Moral Educator Development
Bibliographic record
Abstract
The demand for character development in many of the Western World’s public schools is increasing. Yet there are substantive steps being taken beyond manipulating student behavior in rigidly constructed contexts. Unfortunately manipulating behavior only develops self-interest as the measure of all good and might makes right the legitimation of authority. Yet as any anthropologist can explain it is role-modeling family and village elders that decides which of two instincts will dominate human development: self-interest or cooperation (Tomasello, 2019). As Aristotle famously observed, it makes no small difference what habits humans develop rather, it makes all the difference. But to be truly conducive to moral development those habits must reflect autonomous conviction to develop organizational well-being over the pandemonium self-interest leads towards. The Moral Self-assessment Protocol discussed herein creates the conditions for teacher and other leaders to track their own moral development to role model for those growing into organization membership, in schools, cities, states, countries and businesses.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".