Bibliographic record
Abstract
This paper examines how migration crises in North America are represented in two award-winning interactive documentary projects—Borderland and Roxham. Through various combinations of images, texts, video, surface designs and visual presentation, those two both portray detailed stories and imply critical point of views. Based on reflection on interactivity, hypertextuality, multimediality that afforded by digital technology, this project is guided by the main research question: how do features of interactive documentary enable this non-linear storytelling structure to be a new form of public narrative? With the analysis of Borderland and Roxham as case studies, remixes of multimedia visual elements, narrative structures, contributions from users and variation in user’s narrative routes are also discussed within sub-questions: how does the story of individual open emotional dialogue in those two projects? In what way users can experience the shared values through emotion that may lead to moral choice? Two projects are analyzed by Multimodal Critical Discourse Analysis to evaluate the structure of stories and interactivity constructed by moving images, perspectives, and storytelling. By tapping into the interlocking plots and the power of narrative, I argue that interactive documentary forms a unique public narrative when tackling social issues, allowing self-experience to intertwine with collective experience, and let individuals meet in this temporal public sphere through authorial expressivity, narrative routes and interactive participatory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".