Bibliographic record
Abstract
Before discussing the evidence and significance for political science of shamanism in the late or Upper Paleolithic period (~50KYBP-10KYBP), I would like to make two preliminary points. The first concerns the meaning of Voegelin's use of the term mysticism and why it can, with caution, be applied to shamanism. The second concerns the issue of why political scientists might be interested in prehistoric, or as we now say, early historic periods, which includes the late Paleolithic, the focus of this paper, but also the Neolithic (10KYBP-5KYBP). Voegelin became interested in prehistory during the late 1960s, though arguably his concern with human symbolism and consciousness, which would include prehistoric consciousness, began forty years earlier. In any event, we conclude this first section with a discussion of a few of his relevant remarks prior to his encounter with Marie Konig in the fall of 1968. The next section deals with her arguments and why Voegelin found them attractive. In the final section we examine some of the recent discussions of Shamanism, chiefly by David Lewis-Williams and his colleagues (and criticism of their views) in the context of the late Paleolithic. I might add that this paper is intended to be even more exploratory and suggestive than is usual even for the EVS. Given the existing controversies among specialists and the enormous amount of material still to be digested, in no way is it intended to be conclusive.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".