Bibliographic record
Abstract
When you read the biographies of incredible individuals that have changed the world, or do a quick search of the Internet for inspirational thoughts on "learning by doing", you will inevitably encounter a countless listing of quotes, attributed to notable people such as Doris Lessing, Albert Einstein, or Nelson Mandela.Lessing, for example, winner of the Nobel Prize in Literature, and leader of campaigns against nuclear arms and South African apartheid, once said that "What matters most is that we learn from living."Albert Einstein, physicist, creator of general and specific theories of relativity, and probably one of the greatest -if not best-known -scientists of the 20th Century, stated that, "Setting an example is not the main means of influencing others, it is the only means."Nelson Mandela, anti-apartheid activist and leader of the African National Congress, is credited with saying that "Education is the most powerful weapon you can use to change the world."And while the words from these and other outstanding leaders are motivational, Aboriginal leaders and community economic development officers and change agents continue to add to the growing list of experiential commentary and inspirational quotes.Many of these may be found in the materials published in JAED's "Lessons from Experience."In this section we hear from Canadian Aboriginal leaders, educators, and development officers about learning from living, setting healthy and sustainable examples, and changing the world through education, among other quotable-quotes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".