Bibliographic record
Abstract
Knowledge does not exist as an isolated “piece” of knowledge. Knowledge exists in an aggregated collective state. I define knowledge as a capacity for social action and as a model for reality, as the possibility to set “something in motion”, for example, to solve a task, to produce a material object such as a semiconductor chip or to be competent to prevent something from occurring, for example, the onset of an illness. In this sense, knowledge is a universal human phenomenon, or an anthropological constant. This definition of the term “knowledge” is indebted to Francis Bacon’s famous observation that knowledge is power, a somewhat misleading translation of Bacon’s Latin phrase: scientia potential est. A basic assumption should be that knowledge is not a priori practical. The transformation of knowledge as an ability to act into practical knowledge requires congenial circumstances, such as power or authority that dictates the concrete conditions for action. In this con text, it is helpful to ask about the increasingly prominent role of algorithms (intellectual technology) in relation to knowledge such as ChatGPT software as well as contentious issue of the relation/difference between knowledge and information.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".