Summary and Advocacy: Fifteen Foundations and Twelve Guidelines for Rebuilding Theory, Story, and Our World
Bibliographic record
Abstract
If we take a careful look at what happened to our species scientifically and socially during the 20th century a rather unsettling fact quickly becomes apparent. It is that we are entering this awesome 21st century laden with immense challenges and the most serious kind of questions bearing on the human future with a scientific theory of evolution based almost entirely on the study of the past and the prehuman and the subhuman. Is this really true? What about the books one can think of that deal with human evolution and the future? But how many of these books have become part of the established or mainstream paradigm for evolution theory? By this I mean what is generally taught in schools and generally thought of-even by most scientists-as evolution theory. After a moment's thought, I believe anyone sensitized to the problem I am getting at will conclude I am not over-stating the case: that for the whole of science today supposedly bearing on evolution an emphasis on the human and the future still remains a mere fraction of a whole in which the central tendency is as I have stated it. This is the crux of the crisis in the evolution of our species and the crisis in the development of evolution theory that lies behind the Toronto papers. Here, we look at the 15 foundations and 12 guidelines these papers identify for building the fully human theory of evolution needed to end or resolve this closely interwoven pair of crises.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".