Bibliographic record
Abstract
Changes in business and social environments have led society towards a complex landscape in which the relationship between mainstream media and participatory culture is completely changed, with a consequential blurring of boundaries between public and virtual space. As audience media habits are changing, a digital vision of reality is rising and engagement practices are evolving. As a consequence, there is the need for a new design methodology based on different skills working together. It is then necessary to adopt a disruptive approach to overcome the contemporary complexity, assuming storytelling activities, narrative practice and relationships among people as driving forces for innovation. The cases of Imagine Milan (2009-2012) and Plug Social TV (2013-ongoing), in which we tested listening and expressive tools, and communication strategies in order to activate a dialogue among communities. On the one hand, there is the aim of experiencing audiovisual languages through different narrative formats. On the other hand, we explored the use of stories in a collaborative process, spreading the narrative worlds across different channels. The aim of this paper is to describe our design approach, merging together tools and skills from different areas: communication design strategies as participative methods are linked to codesign actions; branding strategies, coming from the advertising field, as tools for identity development; audiovisual language considered as a cultural interface for listening to reality; transmedia practice as a cultural paradigm able to involve the audience into meaning-making processes; ultimately, social media advocacy is used to build relationships between virtual and real communities.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".