Bibliographic record
Abstract
If we consider the publication of Bandersnatch (Slade) by Netflix as a watershed moment for interactive digital narrative (IDN), are we to believe that we have now moved into a golden age or do we remain in an age of discovery? Tom Abba, in the Journal of Media Practice, situates the moment of publication of his article as a turning point for IDN, though Abba concludes that the degree to which experiments in IDN have been enabled has, up to 2008, been extremely limited. Now, some twelve years later and three years’ post-Bandersnatch, the opportunity to experiment has finally been granted. A group of second year media production students at a UK university did so in collaboration with Stornway.io (an online IDN story map editor). This paper revisits Abba’s 2008 article to reflect on issues that emerge from the experience of introducing IDN to undergraduate students on a program of study. Initial findings offer insights into the sequencing of the development process alongside an emerging framework for the generation of an aesthetic of dramatic agency. Re-evaluating the differences between IDN and other forms of interactive experiences establish a means for thinking about IDN as a distinct, unique practice. In conclusion, this paper poses a final question by asking if we can reconsider IDN in terms of what we have watched rather than, as Abba terms it, what we might watch.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".