Finger Amputation in the Ethnographic and Archaeological Records
Bibliographic record
Abstract
Abstract Several published studies indicate that amputating finger segments, and even multiple entire fingers, for nonmedical reasons has been a surprisingly common practice over the past few hundred years. This chapter reports the results of a study undertaken (a) to shed further light on the occurrence of finger amputation via a survey of ethnographic and historical documents and ethnographic and archaeological objects, and (b) to review the ethnohistoric literature to identify motivations for amputating healthy fingers. Based on information gathered from six online resources using keywords from seven different languages, the survey of ethnohistorical texts revealed that at least 181 remarkably diverse societies engaged in finger amputation as a cultural practice, and that it was not limited to a particular geographic region or type of society. The search for finger amputation-related ethnographic and archaeological objects produced evidence that the total number of societies that engaged in finger amputation in the past may be over 200 and suggests that the practice has a time depth of thousands, possibly even tens of thousands, of years. In addition, the chapter identifies seventeen different reasons for engaging in it other than trying to solve a medical problem with the targeted finger. Among the most common of the nonsurgical motivations were mourning a deceased loved one, appealing to a deity for assistance, and punishment. Taken together, the findings reported in this chapter demonstrate that finger amputation was a widespread cultural practice in the past, one that was invented multiple times, in multiple places, for multiple reasons.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".