Innovation in the Application of Digital Tools for Managing Uncertainty: The Case of <scp>UK</scp> Independent Film
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it