Bibliographic record
Abstract
This article responds to the 4th Interactive Film and Media International Conference (IFM 2022) themes of eco-media, epistemologies and listening by focusing on the role that interactive documentary can play in addressing the existential and pressing issue of climate change. It re-visits the article Interactive Documentary: setting the field which I co-authored with Sandra Gaudenzi in 2012 in light of this now central concern and asks how the affordances of interactive documentary can be used to help re-frame our engagement with the human and the non-human. It places metamodern and polyphonic thinking at the centre of this discussion, as two key concepts that I consider to be particularly helpful when thinking about the contribution that interactive documentary can make to wider debates about eco-media. The paper argues that metamodernism and polyphony can contribute to the development of transformative approaches to interactive documentary and indeed to interactive narrative more generally. This is important as it offers us a set of cognitive tools through which to think about how to navigate the complexities and uncertainties of our collective futures which climate change is undoubtedly bringing. The paper engages with the theoretical arguments first and then applies these to a discussion of my work as a co-convenor of the Polyphonic Documentary project before bringing these thoughts together as tentative conclusions and unresolved issues.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.513 | 0.194 |
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".