An Interaction Ritual Theory of Social Resource Exchange: Evidence from a Silicon Valley Accelerator
Bibliographic record
Abstract
Recent research on start-up accelerators has drawn attention to the central importance of social resource exchange among peers for entrepreneurial success. But such peer relationships contain both cooperative and competitive elements, making accelerators a prime example of a mixed-motive context in which successful generalized exchange—unilateral giving without expectations of direct reciprocity—is not a given. In our ethnographic study of a Silicon Valley accelerator, we sought to explore how generalized exchange emerges and evolves over time. Employing an abductive, sequential mixed-methods approach, we develop a process model that helps explain how a system of generalized exchange may or may not emerge. At the core of this model are the interaction rituals within social events that come to create distinct exchange expectations, which are either aligned or incompatible with generalized exchange, resulting in fulfilled or failed exchanges in subsequent encounters. Whereas fulfilled exchanges can kickstart virtuous exchange dynamics and a thriving generalized exchange system, failed exchanges trigger vicious exchange dynamics and an unstable social order. These findings bring clarity to the puzzle of how some generalized exchange systems overcome the social dilemma in mixed-motive contexts by highlighting the central role of alignment between structure and process.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".