Learning about Failure from Successful Ecosystems
Bibliographic record
Abstract
The evolutionary model of competitive selection is hard to translate in healthcare where current culture, incentives and policies often lead to a failure to check if something works and act on the results. This is particularly problematic in areas of high uncertainty (and corresponding high risk of failure for any proposed strategy), like the care of people with complex needs. We look to the software sector as an example of a human ecosystem experiencing an explosion of diversity that facilitates participation of people from varied backgrounds and has strong selection processes and approaches to manage uncertainty. Key lessons from this sector include facilitating failure through rapid tests with ready alternatives, support for people (not just ideas) so they can try different approaches and a system-level portfolio investment to account for high likelihood of failure of any given project. A successful ecosystem in healthcare would not only select proven strategies, but promote collaboration among innovators so that there is cumulative system learning as opposed to personal empire building. Given the major fiscal, social and demographic challenges on the horizon, failure to search for novel solutions is a bigger risk than trying new things that might not work.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".