Navigating the landscape of pedagogical training: The journeys of three mathematics graduate students
Bibliographic record
Abstract
This article describes the landscape of teaching assistantships (TAships) in the Mathematics Department of a large, public, research institution. First, we present visualized data describing the terrain for all mathematics graduate students. Second, we focus on three specific journeys in that terrain. We employ an autoethnographical research methodology to analyze the pedagogical paths of three recent graduates through written reflection. We highlight some surprising themes that emerge, identify key moments in each reflection, and make three proposals, applicable in broader contexts, to capture and confer their benefits. Dans notre article, nous traçons le portrait de l’assistanat d’enseignement dans le département de Mathématique d’un grand établissement public de recherche. Tout d’abord, nous présentons des données sous forme visuelle de manière à décrire la réalité sur le terrain des étudiants en mathématique des cycles supérieurs. Ensuite, nous portons notre attention sur trois parcours particuliers. Au moyen de réflexions menées par écrit, nous utilisons une méthodologie de recherche autoethnographique afin d’analyser le parcours pédagogique de trois nouveaux diplômés. Nous soulignons certains thèmes surprenants, nous dégageons les moments clés de chaque réflexion, puis nous énonçons trois propositions – lesquelles peuvent être utilisées dans un contexte plus large – et nous en présentons les avantages.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".