A Model of Cross-Disciplinary Communication for Collaborative Statisticians: Implications for Curriculum Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ability to bridge multiple disciplines is critical to the successful practice of collaborative statistics, yet theliterature on statistical education devotes relatively little attention to how this skill can be taught. Our goal here is todescribe a general conceptual framework within which a curriculum on communication and leadership couldultimately be organized.The primary research question pertains to whether an actionable model of cross-disciplinary communication forcollaborative statisticians can be developed, and our task here is to describe such a model and also to illustrate its use.Within this model most communications either share or request information. For example, statisticians might provideinformation about statistics (e.g., specific statistical approaches, general statistical principles), comment on theclinician’s understanding of statistics, share their understanding of clinical content, and request information (e.g.,about clinical content, the design and execution of the study being discussed, etc.). Clinical investigators contributean analogous set of components. In addition, a critical element to the interaction is the higher-level task ofdeveloping a mutually understood agreement about the work to be performed: in essence, proposing and negotiatingsuch an agreement.The model is illustrated using a case study, and general qualitative feedback from investigators who performed thecase study was obtained, commenting on both successful and unsuccessful interactions with statisticians.Implications for curriculum development are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it