Cell-Gazing Into the Future: What Genes, Homo heidelbergensis, and Punishment Tell Us About Our Adaptive Capacity
Bibliographic record
Abstract
If we wish to understand how our species can adapt to the coming tide of environmental change, then understanding how we have adapted throughout the course of evolution is vital. Evolutionary biologists have been exploring these questions in the last forty years, establishing a solid record of evidence that conventional, individual-based models of natural selection are insufficient in explaining social evolution. More recently, this work has supported a growing consensus that our evolution, in which we have expressed extra-ordinary adaptive capacities, can best be explained by “Multi-level Selection”, a theory that includes the influence of both genes and culture to support unique adaptive capacities premised on pro-social behaviours and group selection, not individual-level competition for survival. Applying this scholarship to contemporary concerns about adapting to environmental change may be quite fruitful for identifying sources of vulnerability and adaptive capacity, thereby informing efforts to enhance the likelihood for sustainable futures. Doing so, however, requires that we bridge the gap between evolutionary biology, and the social sciences study of sustainability.
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 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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".