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
The development and maintenance of expertise in any domain requires extensive, sustained practice of the necessary skills. However, the quantity of time spent is not the only factor in achieving expertise; the quality of this time is at least as important. The development and maintenance of expertise requires extensive time dedicated specifically to the improvement of skills, an activity termed deliberate practise. Unfortunately, determining how to engage in this deliberate practise is not obvious for tasks such as diagnosis, which involve high stakes and are predominantly cognitive nature. Reflection on and adaptation of one's cognitive processes is important; this could be supplanted by seeking out the opportunity to engage in trial and error in low risk environments such as simulators. Regardless, most individuals tend to favour well-entrenched activities and avoid practise. This may be due to lack of awareness of deficiencies in performance. However, it may also be due to the individual's conception of the nature of expertise. Although expertise requires experience, experience alone is insufficient. Rather, the development of expertise is critically dependent on the individual making the most of that experience. As a result, motivational factors are fundamental to the development of expertise. Overcoming deficiencies in self-monitoring is not a sufficient remedy. It is also necessary is that clinicians form an attitude toward work that includes continual re-investment in 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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