Bibliographic record
Abstract
This book examines how predicting the future based on analytical and empirical models is essential for our species' survival, stable organization, and well-being. At a high level, information exchange, mainly through communication between people, fosters a deeper understanding of reality and strengthens human bonds, ultimately contributing to the emergence of societies as new creatures or "superorganisms" with significantly more data to feed those models. Communication is fundamental to the development of complex organisms, the maintenance of internal order, and the propagation of knowledge via cultural packets. The human brain's plasticity, memory, and learning capabilities enable storage and rapid responses to new information, enhancing our decision-making and forecasting abilities. The book discusses the complexity of multicellular organisms and the mechanisms that coordinate their internal subsystems, highlighting the emergence of hierarchies from fundamental particles to networks of superorganisms. The interconnectedness of biological oscillators with prominent similarities enables synchronization as a foundation for their superior organization and collective agreements. Finally, this book explores the human quest to enhance survival through accumulating knowledge and developing tools and technologies. It underscores the significance of artificial intelligence as a tool to support cognitive abilities and address global challenges while acknowledging our limited control over reality within an independently operating universe.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.246 | 0.134 |
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".