Bibliographic record
Abstract
This video by Dr. R. M. Fisher, current founder and director of The Fearology Institute, covers his fearological perspective on the new paradigm of ethical leadership towards fearless leadership by various great political leaders, the focus particularly on Marianne Williamson who has just announced her campaign to run for president of the USA in 2020. This is part of the Fearlessness Movement, since the beginning of human history, according to Fisher and we best give this a lot of study and attention and explore how to make it a successful teaching for all. Fisher offers his own approach and philosophy to the campaign and calls for a united front to help all people move from fear-based living to fearlessness, from a culture of coping and fear to a healing culture of fearlessness. He says it is very important to support the feminine, feminist, womanist leaders, like Marianne Williamson. He critiques briefly the labeling of "sociopathic" as used by Williamson and others for the 'enemy' and says we need a lot more questioning about that strategy and its usefulness-- such labeling is typical of a fear-based perspective itself. We need a fearlessness-based way of thinking through this problem, like all other difficult problems in society. Fisher, is planning a Part 2 video soon... ABC News just did a feature on MW's campaign well worth watching: https://abcnews.go.com/Nightline/vide...
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 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".