Why heads matter in palaeoanthropology: The impacts and consequences of collecting skulls
Bibliographic record
Abstract
This piece reflects on the importance of and focus on heads – especially the collecting of skulls and its impacts – in alpha taxonomy, biological anthropology, and Western science more broadly. We consider how the announcement and overall discovery story of the Taung Child revolutionised our understanding of hominin cranial evolution, but also fit within these skull-collecting objectives and contributed to the palaeoanthropological fixation on the skull. We contextualise this within the history of ‘physical’ anthropology in light of its initial goals in scientific racism, and consider how this process of skull collecting has become normalised in the discipline as a result of this history. As evidence for this, we quantify the possible effects of skull-collecting by collating available data on the number of skulls versus post-crania curated in a representative South African collection and compare the number of skulls versus post-cranial hominin fossils that form part of species hypodigms. We also explore how the ownership of skulls and ownership of narrative in the discipline have been intertwined throughout its history. Finally, we focus on how this early overemphasis on skulls, and especially brain size/intelligence, may have skewed our understanding of human evolution and contributed to ideas of human exceptionalism.
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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".