Bibliographic record
Abstract
What makes the human mind unique? One answer would be our particular kind of culture, which might be called ‘mindsharing’ culture. Human beings are not only able to detect the existence of other minds, and to understand that those minds have beliefs, but are also able to form networks of trust built around shared intentions and beliefs. No other species does anything like this. Much current research in neuroscience is aimed at understanding the processes that contribute to our construction of culture. Recognizing the importance of integrating this work into their research, and of drawing neuroscientists into more collaboration, the McDonald Institute for Archaeological Research at the University of Cambridge initiated a conference in September 2007, devoted to the theme ‘Archaeology meets neuroscience’. A special issue of the Philosophical Transactions of the Royal Society is now devoted to the proceedings of that pioneering meeting. Although understandably selective, this volume contains a smorgasbord of current ideas and research from philosophy, psychology, anthropology and archaeology. The selection of papers is diverse and stimulating. This relatively new marriage of disciplines still lacks a unifying framework, but one must start somewhere, and no time like the present. A major link between archaeology and neuroscience is provided by cognitive science, which has a foot in both camps. Some aspects of cognition, such as literacy, mathematics and music are obviously cultural in origin. Others, such as attention, perception and action stem directly from the design of the central nervous system. These two influences, brain and culture, work together in forming human cognition, and cognitive scientists find themselves in the position of having to explain many of the higher cognitive capabilities of human beings in terms of hybrid brain-culture mechanisms. Evolutionary models are one important way of ordering the evidence on hybrid mechanisms, and epigenetic factors may …
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".