Bibliographic record
Abstract
Craig Newell, a well loved friend, participant in, and supporter of Complicity and its founding organization, Complexity Science and Educational Research (CSER), passed away on March 27th, 2008 due to esophageal and stomach cancer.Craig touched the lives of many people-students, colleagues, friends, family-and will certainly live on through the lives of all those he influenced.His lifelong passion for mathematics and education, his engaging manner, excellence in teaching and thoughtful philosophical approach inspired his students to be able not only to do math but also love it.He also had a passion for sports where the teacher came out in him again as he coached generations of kids in track and field and cross country.A tribute to Craig, by one of his former student, follows this article.With more than twenty-eight years of teaching mathematics and other subjects in high school classrooms, and at an age where others retired to travel or pursue hobbies, Craig's love of education was such that he retired to pursue a Doctorate in Education at Simon Fraser University (SFU).The work of Brent Davis and Dennis Sumara, in particular, captured Craig's interest and encouraged his doctoral study applications of Complexity Theory to mathematics education.Doctoral study, however, did not preclude a travel adventure with kayaking and zip-lining in Costa Rica as learning experiences, for Craig, were not limited to the classroom but came in many wonderful forms.Craig was attracted to the SFU doctoral program, in particular, because of his deep conviction regarding the interdisciplinary nature of learning.He was delighted that the
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.300 | 0.086 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".