Emerging Forms of Citizen Video Activism: Challenges in Documentary Storytelling & Sustainability
Bibliographic record
Abstract
Gilles Deleuze's early reflections on assemblage identify the idea of the diagram or possibility space as a framework to suggest the ways in which the assembling of technology and human practices merge to create distinctive and innovative new assemblages. Yet routinely it is the technological advances of the 21st century that receive the most revered credit for shifts within citizen-based video activism. Essential to the new and often undefined waves of digital documentary birthed in scattered alcoves of social activism and human rights movements are the relationships between the components of these assemblages. Particularly influential are the facilitating agents spearheading the means to digital video literacy that allow these narratives to be shared. Conducted over three years, my Ph.D. research has examined very specific emerging video practices rooted in social activism in a number of global settings. My fieldwork has sought out citizen media makers in order to discuss how these practitioners have approached their nascent video activism with the goal of identifying properties that might allow these surfacing video practices to become sustainable over time. This paper examines and critiques specific elements that these particular forms of video activism confront in their own unique global possibility spaces. Moreover, as traditional methods of video distribution and video recording continue to change even further through online platforms and mobile technology, how might we begin to identify emerging forms of citizen-based video activism and documentary media?
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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.032 | 0.036 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".