Digital Evidence: Revisiting Assumptions at the Intersection of Technology and Assessment
Bibliographic record
Abstract
The increasing use of technology in health care and health professions education is an invitation to examine how digital sources of evidence are used in making assessment claims. In this paper, we describe how four sets of terms-primary and secondary data; structured and unstructured data; development and use; and deterministic and generative-can aid in examining how data from digital sources are used in evaluating what learners know and can do. Drawing on multiple examples, this paper shows how the four sets of terms can help both developers and users of technology-based assessment systems.
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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 | 0.174 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.029 | 0.015 |
| Science and technology studies | 0.007 | 0.176 |
| Scholarly communication | 0.035 | 0.088 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.013 | 0.020 |
| 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".