Towards a Model of Stewardship and Accountability in Support of Innovation and “Good” Failure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From an evolutionary perspective, failures of imagination and missed opportunities to learn from experimentation are as potentially harmful for the health system as failures of practice. The conundrum is encapsulated in the fact that while commentators are steadfast about the need on the part of the stewards of the health system to avoid any waste of public dollars, they are also insistent about the need for innovation. There is tension between these two imperatives that is often unrecognized: the pursuit of efficiency, narrowly defined, can crowd out the goal of innovation by insisting on the elimination of "good waste" (the costs of experimentation) as well as "bad waste" (the costs of inefficiency) (Potts 2009). This tension is mirrored in the two broad drivers of performance reporting in health systems: public accountability and quality improvement. Health organizations, predominantly funded by public funds, are necessarily accountable for the ways in which those funds are used and outcomes achieved. This paper reviews how accountability relationships should be re-examined to create room for "good failure" and to ensure that system accountability does not become a barrier to performance improvement.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it