Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Storytelling is a powerful tool that connects us and shapes our understanding of the world. Theories of effective storytelling boast an intellectual history dating back millennia, highlighting the significance of narratives across civilizations. Yet, despite all this theorizing, empirically predicting what makes a story successful has remained elusive. We propose narrative reversals, key turning points in a story, as pivotal facets that predict story success. Drawing on narrative theory, we conceptualize reversals as plot: essential moments that push narratives forward and shape audience reception. Across 30,000 movies, TV shows, novels, and fundraising pitches, we use computational linguistics and trend detection analysis to develop a quantitative method for measuring narrative reversals via shifts in valence. We find that stories with more' and more dramatic, turning points are more successful. Our findings shed light on this age-old art form and provide a practical approach to understanding and predicting the impact of storytelling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it