Bibliographic record
Abstract
In this presentation, we present our recent year-long collaborative work in what Steve Mentz calls “the blue humanities” – thinking about the climate emergency in relation to twinned problems of water and fire. We’ll first contextualize our intention as it emerged from our work with the feminist research creation Decameron Collective. We will then demonstrate how we have been using SCALAR to create an interactive documentary/research creation project that employs a range of analogue and digital documentary practices which include book making, watercoloring, embodied documentary filmmaking, and augmented reality, anchoring our approaches in theories of embodiment (“the role of the felt sense and the body politic primarily in the process of making documentary films, and secondarily in the film’s subject matter, role in the media landscape, and impact of process on the filmmaker,”( Monde)) asking what do our bodies already know about climate change?, as well as eco-theory notions of Solastalgia, that is, the distress caused by environmental change (Albrecht et al.), exploring the research questions our experimentations both crystallized and obscured. We will demonstrate how we have used the affordances of Scalar to create an interactive, living, creative and data-driven assemblage to deepen our connections and understanding of global and personal events. Ultimately, our goal will be to provide an assessment of what, if anything, these errant “methods” offer interactive film and media studies, as well as idocs practice and methodologies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.021 |
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".