Bibliographic record
Abstract
The thrust of this book is that pressures and stresses on peripheral populations in areas of population growth have driven the changes that have marked human biological and cultural evolution. It has been the stressed populations that have been the most innovative (e.g. Fitzhugh, 2001). Hominids have responded to the increasing instability with risk reduction responses that fit under the umbrella of increasing the spatio-temporal scale of operation. Correlates of increase in scale have included, as we saw in Chapter 5, an increase in dispersal ability, home range size, group size, neocortex size, the complexity of social behaviour, symbolism, efficient and mobile tool kits, and gracile morphology. Since global climate and environments have become increasingly unstable over the last two million years those marginal populations that adapted to local stresses in these ways were able to turn disadvantage into advantage each time conditions deteriorated or became less stable. These adaptations would have evolved in peripheral populations that perceived marginal landscapes as spatially heterogeneous and therefore spatially risky. These adaptations to exploiting patchy landscapes then became advantageous in situations of increasing temporal heterogeneity, that is in situations that were perceived as temporally risky. We can therefore understand these adaptations as evolving through a normal process of natural selection and we do not need to invoke alternative mechanisms, such as variability selection (Potts, 1996a, b, 1998), to explain the observed patterns and trends. Most of these adaptations would have been behavioural.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".