Bibliographic record
Abstract
Through the aid of ever advancing technology, the analysis of complex phenomena offers us more comprehensive insights regarding the intricate inner workings of Nature’s dynamic processes. Through such digital simulations (i.e., of fluid, aero, neuro and vibratory dynamics), the operations and flow of energy are revealed as highly patterned process-structures of activity. These vivid configurations often resemble and correlate with the patterns and motifs found at different scales throughout Nature and in a myriad of cultural artifacts. As intricately braided cellular relationships, these fertile processes evolve into highly integrative systems with re-generative, shape-shifting and re-structuring capabilities. Moreover, they are robust coalitions of event-filled-processes, highly responsive and fluently encoded with information. This embodied potential of generative kinetic in-formation and related patterns have been explored and offer more comprehensive insights regarding the resonances between self-organization, pattern generation and emergent complex morphology. At the heart of this lies the nature of process-structures and their elaborations into multi-dimensionally entrained kinetic patterns of patterns-in-formation. We are experientially embodied with and inextricably embedded within this interplay of ubiquitous metapatterns with reciprocally related cultural artifacts and motifs offering insightful resonances as analytical tools advance and probe further into the inner workings of the human mind and the nature of embodied consciousness.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".