The utility of failure: a taxonomy for research and scholarship
Bibliographic record
Abstract
INTRODUCTION: Health professions education (HPE) research and scholarship utilizes a range of methodologies, traditions, and disciplines. Many conducting scholarship in HPE may not have had the opportunity to consider the value of a well-designed but failed scholarly project, benefitted from role-modelling of the value of failure, nor have engaged with the common nature of failure in research and scholarship. METHODS: Drawing on key concepts from philosophy of science, this piece describes the necessity and benefit of failure in research and scholarship, presents a taxonomy of failure relevant to HPE research, and applies this taxonomy to works published in the Perspectives on Medical Education failures/surprises series. RESULTS: I propose three forms of failure relevant to HPE scholarship: innovation-driven, discovery-oriented, and serendipitous failure. Innovation-driven failure was the most commonly represented type of failure in the failures/surprises section, and discovery-oriented the least common. CONCLUSIONS: Considering failure in research and scholarship, four conclusions are drawn. First, failure is integral to research and scholarship-it is how theories are refined, discoveries are made, and innovations are developed. Second, we must purposefully engage with the opportunities that failure provide-understanding why a particular well-designed project failed is an opportunity for further insight. Third, we must engage publicly with failure in order to better communicate and role model the complexities of executing scholarship or innovating in HPE. Fourth, in order to make failure truly an opportunity for growth, we must, as a community, humanize and normalize failure as part of a productive scholarly approach.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".