Bibliographic record
Abstract
Debates over the origin and influences of Indo-European languages and societies have fueled nationalistic passions over ancestral homelands as well as more recent attempts to categorize Indo-European language speaking societies as patriarchal, violent, and disruptive of a Neolithic utopia in Old Europe. While archaeological data is generally not abundant or refined enough to explain the distribution of Indo-European languages, it is possible to generalize that religions of herding societies demonstrate surprising similarities, be they the Nuer or the Masai in East Africa and the historical Indo-European cattle herders of the steppes. Their warrior groups adopt powerful wild animals as totems and form exclusive and independent social groups or classes, with their own deities, rituals, myths, and ritual leaders. Priestly elites also form a separate class, which strives to maintain dominant control over raids, spells, and initiations. Thus, in the pastoral nomad type of society, there is an evident conflict of interest between the major power-wielding sectors (warriors versus priestly elites) resulting in problems of integrating both social groups together, not to mention the people that do most of the herding and gardening. In this respect, religion can be adaptive in both defining special interest groups within a society and in integrating groups together. This represents a further politicization of religion, a tendency that began in the Upper Paleolithic. This article is reprinted with permission from Shamans, Sorcerers, and Saints: A Prehistory of Religion (Washington, DC:Smithsonian Institution Press, 2003).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.130 | 0.042 |
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".