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
When I moved from linear documentary production to the newly emerging field of interactive storytelling in early 2000, I was excited by the potentialities of the Web, especially the possibility of co-creation in factual storytelling. Looking back, I can clearly see that what attracted me was the exploration of how factual narratives could make use of two unique affordances of digital media: user agency and interactivity. More than twenty years later, I am still experimenting with ways to use interactivity to facilitate co-creation of reality and move away from the representational tendency of linear documentaries (Gaudenzi 2013). In this paper, I will use the Corona Haikus project (2020) to question the current understanding of user agency in participatory interactive narratives. I have chosen such project because I have personally been involved in it as a co-author, but also as a participant, and therefore I have both co-designed its user’s agency, and experienced it as a user. I will argue that agency in interactive documentary (i-doc) should be considered as a space of user empowerment that does not always have to affect the interactive narrative itself, because it can also be placed outside of the narrated story. The Corona Haikus example will be used to demonstrate that, in participatory narratives, deep individual and societal impact can be designed by mixing different types of mini-agencies and by orchestrating them as a journey of empowerment that is gradual and evolutive. Reflexive and evolutive agencies will be defined and presented as new ways to approach impact design in interactive narratives.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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