LEARNING TO USE STATISTICS IN RESEARCH: A CASE STUDY OF LEARNING IN A UNIVERSITY-BASED STATISTICAL CONSULTING CENTRE
Bibliographic record
Abstract
This paper presents a qualitative case study of statistical practice in a university-based statistical consulting centre. Naturally occurring conversations and activities in the consulting sessions provided opportunities to observe questions, problems, and decisions related to selecting, using, and reporting statistics and statistical techniques in research. The consulting sessions provided simultaneous opportunities for consultants and clients to learn about using statistics in research. Consistent with contemporary theories that emphasize social dimensions of learning, major themes relate to (a) types of clients and consulting interactions, (b) disciplinary and statistical expertise, and (c) the role of material objects and representations. Evidence shows that consultants and clients learned during the consulting sessions and that the statistical consulting centre contributed positively to teaching and research at the university. First published November 2010 at Statistics Education Research Journal: Archives
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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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.022 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".