Bibliographic record
Abstract
In this paper we argue that knowledge in health care is a multidimensional dynamic construct, in contrast to the prevailing idea of knowledge being an objective state. Polanyi demonstrated that knowledge is personal, that knowledge is discovered, and that knowledge has explicit and tacit dimensions. Complex adaptive systems science views knowledge simultaneously as a thing and a flow, constructed as well as in constant flux. The Cynefin framework is one model to help our understanding of knowledge as a personal construct achieved through sense making. Specific knowledge aspects temporarily reside in either one of four domains - the known, knowable, complex or chaotic, but new knowledge can only be created by challenging the known by moving it in and looping it through the other domains. Medical knowledge is simultaneously explicit and implicit with certain aspects already well known and easily transferable, and others that are not yet fully known and must still be learned. At the same time certain knowledge aspects are predominantly concerned with content, whereas others deal with context. Though in clinical care we may operate predominately in one knowledge domain, we also will operate some of the time in the others. Medical knowledge is inherently uncertain, and we require a context-driven flexible approach to knowledge discovery and application, in clinical practice as well as in health service planning.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".