Anna Sierpinska (1947-2023) - outstanding researcher in mathematics education
Bibliographic record
Abstract
On 19 October 2023, Anna Sierpinska, Professor Emerita at Concordia University in Montreal, known internationally as a Polish-Canadian scholar in the field of mathematics education passed away. Anna Sierpinska received her mathematical education in Poland - at the University of Warsaw. She obtained a PhD in mathematics in their didactics at the Wyższa Szkoła Pedagogiczna (WSP) in Krakow (current name: University of the National Education Commission). She was an academic teacher in Poland and then - since 1990 - in Canada. She conducted intensive didactical research relating to various aspects of the learning process in mathematics, and was particularly appreciated for her contributions to research on epistemology and understanding in mathematics. She was an extremely active participant in international efforts to improve mathematics teaching. While already working in Canada, she continued to take a keen interest in the issues of the didactics of mathematics in Poland. She participated in didactic conferences organised in Poland and invited Polish educators to Montreal to hold classes at Concordia University. She promoted Polish research in the field of didactics of mathematics internationally, promoted the journal Dydaktyka Matematyki in foreign environments and provided substantive and linguistic support to less and more experienced Polish doing research in the didactics of mathematics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".