On Abstract Sciences: From Data, Information, Knowledge to Intelligence Sciences
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 emergence of abstract sciences as a counterpart of classic concrete sciences is presented in this work. The framework of abstract sciences encompasses data, information, knowledge, and intelligence sciences from the bottom up. It is found that intelligence is the ultimate level of cognitive objects generated in human brains aggregated from data (sensory), information (cognition), and knowledge (comprehension). However, there is a lack of rigorous studies and coherent theories towards the theoretical framework of abstract sciences as the counterpart of classical concrete sciences. This paper explores the cognitive and mathematical models of abstract mental objects in the brain. The taxonomy and cognitive foundations of them are explored. A set of mathematical models of data, information, knowledge, and intelligence is formally created in intelligent mathematics. Based on the cognitive and mathematical models of the cognitive objects, formal properties and relationship of contemporary data, information, knowledge, and intelligence sciences are rigorously explained.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it