Opportunity, Motivation, and Ability to Learn from Failures and Errors: Review, Synthesis, and Ways to Move Forward
Bibliographic record
Abstract
Although organizations and individuals tend to focus on learning from success, research has shown that failure can yield crucial insights in various contexts that range from small mistakes and errors, product recalls, accidents, and medical errors to large-scale disasters. This review of the literature identifies three mechanisms—opportunity, motivation, and ability—through which individuals, groups, and organizations learn from failure, and it bridges the gaps between different levels of analysis. Opportunity to learn from failure mostly takes the shape of more information about errors and failures that are generated by one’s own and others’ prior failures or near-failures. Motivation to learn from failure is hindered by punitive leaders and organizations. Finally, the ability to learn from failure partly relies on inherent attitudes and characteristics, but can be further developed through thoughtful analysis and transfers of successful routines. Our review leads us to distinguish between erroneous versus correct processes and adverse versus successful outcomes to better understand the full gamut of events that are faced by organizations. We identify the existence of noisy learning environment, where spurious successes (when erroneous processes still lead to successful outcomes) and spurious failures (when correct processes are combined with adverse outcomes) lower the opportunity to learn. Considering noisy learning situations is helpful when understanding the differences between slow- and fast-learning environments. We conclude our review by identifying a number of unexplored areas we hope scholars will address to better our understanding of failure learning.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".