That’s Not What Happened! Sustaining Expectations Through Collective Storytelling.
Bibliographic record
Abstract
From a cultural perspective, narratives are understood as the main mechanism through which actors legitimate novel ideas, practices or forms of organizing. On the one hand, narratives serve as a touchstone by which audiences confer legitimacy. On the other, narratives also set expectations, which in turn set the stage for future disappointments. Answering a call for a better understanding of how narratives are revised in order to maintain or regain legitimacy, I followed the journey of Double Fine - a small independent video game studio - over three turbulent years, from their launch of one of the very first highly successful crowdfunding campaigns to the delivery of the final product. From this longitudinal and qualitative case study, I develop a model of “collective storytelling” to explicate how expectations were sustained over time. In so doing, this paper explores the active role that audiences play in conferring and maintaining legitimacy, which had previously been undertheorized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".