Revisiting the Role of Critical Reviews in Film Marketing
Bibliographic record
Abstract
These are the proceedings of the first COUNTER workshop "Mashing-up Culture: The Rise of User-generated Content", Uppsala University, May 13-14, 2009.COUNTER is a European research project exploring the consumption of counterfeit and pirated goods.Sampling, remixing, mash-ups and appropriation are part of the digital creative milieu of the twenty-first century.Sites such as YouTube and deviantART have offered new outlets for creativity and become hubs for innovative forms of collaboration, thus playing their part in challenging modernist notions of what it means to be a creator as well as a consumer.Drawing on this general background, the ten papers presented in these proceedings are examining areas such as: Sampling, mash-ups, and appropriation; Creativity and collaborative practices; Creative Industries and intellectual property; Copyright, Cultural Heritage and Cultural Policy; and Formal and informal regulations of intellectual property.The ten authors are all contributing to an international and interdisciplinary scientific discussion on the mash-up.Mash-ups and user-generated content are social and cultural phenomena which in these papers are put into various contexts, from legal ones, over technology, to the nation as a framework.
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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 | 0.103 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".