Hominid growth and development: The modern context
Bibliographic record
Abstract
Arm length varies among humans, and some people must have longer arms than others. The average chimp has a longer arm than the average human, but this doesn't mean that a relatively long-armed human is genetically similar to apes. Normal variation within a population is a different biological phenomenon from differences in average values between populations. (Gould, 1981: 127) Introduction The above quotation, from Stephen Jay Gould, is a good starting-point for any discussion of the evolution of human ontogeny. This is because we must integrate an understanding of within- and between-population variation in addressing the questions of when and how the modern human pattern of growth and development first appeared. However, before we can even attempt to address this question, we must first consider: What is the modern human pattern of growth and development? What aspects of growth and development make modern humans unique? Inevitably, what we attempt to do is to characterize what constitutes the average pattern. Such an approach, to typify a species, is a common practice in paleoanthropology. We use similarities, differences, and unique features to distinguish between different species, and even genera. Hence the practice of using a “type specimen,” an individual fossil against which all others are compared to determine their species attribution, in the naming of a new fossil species. A similar approach is used in auxological paleontology (Bogin, this volume; Tillier, 2000), a term used to describe research into patterns of growth and development of fossil species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".