Bibliographic record
Abstract
In this paper, Kant's philosophical doctrine of the categories of the reason is used to substantiate the conceptual model of knowledge representation, based on the collective interaction of a lot of intellectual atomic elements of knowledge (knowledge quanta), which are combined into clusters like neurons in the brain; and also a phenomenological description of the corresponding universal ontology, proceeding from the philosophical premise of Husserl-Heidegger that the meaning of intelligence is not so much in knowing the absolute truth as in survival, is presented. In the process of cognizing the surrounding world, a person uses both a priori knowledge and a posteriori knowledge, but the transcendental content of a priori forms of thinking does not allow them to be used directly in logical judgments. Nevertheless, one can try to use them as "ontological predicates" following the advice of I. Kant, what was done in this article. Heuristic ontological relations that directly follow from the categories of Kant are easy to use and sufficient to describe any ontology. Offered knowledge representation model, the key idea of which is the primacy of knowledge to logical inference and their emergent ability to self-organize, in conjunction with the transcendental logic-based ontology of empirical knowledge can be used to create a universal inference engine.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".