Innovation in the Application of Digital Tools for Managing Uncertainty: The Case of <scp>UK</scp> Independent Film
Bibliographic record
Abstract
This research investigates innovation in how film producers use social digital tools to engage consumers, reduce demand uncertainty and respond to the challenge of digital disruption that affects the traditional film value chain. Through three empirical case studies of film production and exploitation, we examine examples of innovation in product, service, distribution, marketing and process, each having important implications at the organizational level. Our findings show that innovations in one area have important implications for other areas, distribution impacting on concepts of product and service, for example. We also show that internal firm micro‐process dynamics impact directly on external interactions between the firm, consumers en masse and partner firms. Our research thus lies at the nexus of innovation, social media and uncertainty management, and questions the boundaries found in innovation ‘types’ or dominant taxonomies in traditional R&D frames.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".