Standardized Assessment of Reasoning in Contexts of Uncertainty
Bibliographic record
Abstract
Current written tools of assessment are mostly measuring the capacity to solve well-defined problems by the application of rules and principles, while the essence of expertise in the professions lies in the capacity to solve ill-defined problems, that is, reasoning in contexts of uncertainty. The purpose of this study is to describe an approach that allows assessing ill-defined problems and to present and discuss research findings related to this approach. The tool has been used up to now mainly in medicine, however it can be applied in all health professions. The approach is based on three principles: (a) examinees are faced with a challenging authentic situation in which several options are relevant; (b) the response format is a Likert-type scale that reflects the way information is processed in problem-solving situations, according to the script theory; and (c) scoring is based on the aggregate scoring method to take into account the variability of reasoning processes among experts. Research findings suggest that the approach permits one to reliably discriminate examinees across their level of experience, and so in very different domains. It makes it possible to measure skills or domains that were up to now difficult to measure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".