A Little (up)SET THEORY: A Philosophical and Psychological Pondering of a Scientist on the State of Our Art
Bibliographic record
Abstract
Ever since Thomas Kuhn’s brilliant postulate that scientific progress is generated in the context of paradigms (commonly accepted notions), and that scientific revolutions manifest as paradigm shifts, we not only work along paradigms but are striving to consciously create them. Moreover, paradigms emerge not only within science but also about science, i.e., paradigms about how science should be done and how it should interact with society. In our field, the currently reigning paradigm (or more properly perhaps meta-paradigm or zeitgeist) is undoubtedly that of knowledge translation. Indeed, knowledge translation (KT) is a crucially important concept. And yet when a paradigm or rather its application becomes a self-imposed dogma or an overbearing cliche, it may counteract progress. When one receives criticism that one’s work is “cutting edge research in an enigmatic area in cell biology but the explanation how it is translational to clinic is weak,” one starts pondering the zeitgeist that compels referees to assess everything from the KT angle. And during such pondering over the current state of affairs, it suddenly dawned on me that the very concept of knowledge translation as it is applied today, is fundamentally—philosophically—flawed. In fact, it is a misnomer. What is being forced on us is not knowledge translation but science translation. And of course, knowledge and science are not the same. Far from it. Let me explain.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.140 |
| Scholarly communication | 0.021 | 0.043 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".