Who Can Change the Narrative? Network Centrality and the Evaluation of Unconventionality
Bibliographic record
Abstract
Full title: Who can Change the Narrative? Network Centrality and the Evaluation of Unconventionality in the Feature Film Industry. Abstract: In this paper, I develop a social structural theoretical perspective to investigate under which circumstances unconventional cultural products can elicit positive responses from the audiences that evaluate them. I start from recognizing the double-edged sword nature of unconventionality for the trajectory of cultural products that embody it. On one hand, highly unconventional products can suffer from an illegitimacy discount, and therefore be penalized. On the other, they sometimes succeed and become breakthroughs. I argue that, when evaluations of cultural products are formulated by audiences of peers that are socially connected to the evaluated actors, actors’ social network centrality is a resource that can be mobilized to influence the evaluators’ propensity to positively evaluate potential instances of unconventionality. I evaluate this conjecture in the context of eight different awards in the North American film industry, on a sample of more than 2900 films released between 1993 and 2016. I consider how the likelihood that a film receives a nomination or award relates to the film crews’ centrality in the industry collaboration network, as well as to a measure of the degree to which it deviates from the narrative conventions that dominate its genre. I discuss findings, empirical limitations, and implications for research on the evaluation of novelty and the interplay of networks and culture.
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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 | 0.008 | 0.037 |
| 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 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".