Bibliographic record
Abstract
Stemming from a process of non-linear narratives in a digital convergence landscape, interactive documentary proposes an innovative change in the documentary realm by allowing the user to choose how to consume the contents and produce a universe of narrative possibilities where the stories begin and end by linking to each other. This paper examines to what extent interactive documentary may constitute a voice of process (Couldry 2), assuming to be a resource that may contribute to social change by seeking awareness of gender violence and justice for the victims. The empirical study focuses on two interactive documentaries approaching violence against women: Mujeres en Venta and The Quipu Project. The methodological approach draws upon a three-fold dimension: discourse analysis, multimodal analysis, and the interaction structure. Results show that both projects explore user’s interaction and participation to favor engagement and immersion with the narrated reality, aiming to promote social change. The empirical study has identified that the two documentary projects use narrative resources from traditional documentaries and simultaneously introduce relevant novelties to the perspective of user interaction and participation, aimed at favoring the engagement and immersion with the narrated reality. Mujeres en Venta and The Quipu Project propose a multilevel communicative flow, which encompasses three combined dimensions: aesthetic, narrative, and emotional (Mora-Fernández 198–200).
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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 | 0.002 | 0.006 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".