New Agents of Innovation: Autonomous Technology and the 3000-Year-Old Ritual of Scapegoating
Bibliographic record
Abstract
Drawing from 15 years of archival data on the 2008 financial crisis and the 2022 blockchain crisis, I extend interaction ritual chain theory to recognize the effect that the Internet of Autonomous Things (IoAT) has on interaction rituals and the conjoined relationships that form between humans and autonomous technologies through these interactions. I find that IoAT changes the way that humans enact the 3000-year-old ritual of scapegoating through the constructs of branching and nesting. When compared to historic rituals, new IoAT scapegoating rituals generate positive emotional energy, and increase trust. I integrate my findings into a novel theoretical framework that captures the effect of IoAT on interaction rituals through the constructs of “branching” and “nesting.” This research contributes to our understanding of the emerging role of IoAT agency, introduces the construct of IoAT interaction rituals and proposes conjoined-agency between IoAT and humans as an emerging construct in organizational and social environments.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".