Material Affective Engagements: Examples from Ancient Mesopotamia
Bibliographic record
Abstract
This article applies approaches from current emotion research on material affective scaffolds—objects made and used to enhance, and more generally transform, affective states—to the emerging field of study focusing on emotions in ancient Near Eastern societies. Its main goal is to extend the framework of 4E cognition—with its central notion that human cognition is embodied, embedded, enactive, and extended—to the realm of affective states, emphasizing that through our bodily interactions with material objects we transform not just our cognitive processes but also our emotions, moods, and so forth. Thereby, the present study seeks to contribute to the exploration of the relationships between sensory experiences, emotions, moods, and the material world by investigating the affective meanings that material things acquire through people’s entanglements with them. The study focuses on one particular class of objects—Mesopotamian amulets from the first millennium BC, which served as bodily adornments but were also understood to have the power to evoke affective responses through their activation in ritual performances. Referring to scholarly compendia in Mesopotamian cuneiform texts, this study demonstrates that these objects were recommended by healing experts to influence different affective states, both in oneself and others. It examines the connection between affective states and specific material features of the amulet components (consisting of minerals, metals, and plant and animal substances). Finally, Mesopotamian views of affective states and their management are compared with those of contemporary cognitive-affective science. This comparison shows that although there are some analogies, there are also important differences that depend mainly on different understandings of the human mind and agency. byUlrike SteinertJohannes Gutenberg University Mainzusteiner@unimainz.deandGiovanna ColombettiUniversity of ExeterG.Colombetti@exeter.ac.uk
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".