Bibliographic record
Abstract
What’s a Cellphilm? explores cellphone video production for its contributions to participatory visual research. There is a rich history of integrating participants’ videos into community-based research and activism. However, a reliance on camcorders and digital cameras has come under criticism for exacerbating unequal power relations between researchers and their collaborators. Using cellphones in participatory visual research suggests a new way forward by working with accessible, everyday technology and integrating existing media practices. Cellphones are everywhere these days. People use mobile technology to visually document and share their lives. This new era of democratised media practices inspired Jonathan Dockney and Keyan Tomaselli to coin the term cellphilm (cellphone + film). The term signals the coming together of different technologies on one handheld device and the emerging media culture based on people’s use of cellphones to create, share, and watch media. Chapters present practical examples of cellphilm research conducted in Canada, Hong Kong, Mexico, the Netherlands and South Africa. Together these contributions consider several important methodological questions, such as: Is cellphilming a new research method or is it re-packaged participatory video? What theories inform the analysis of cellphilms? What might the significance of frequent advancements in cellphone technology be on cellphilms? How does our existing use of cellphones inform the research process and cellphilm aesthetics? What are the ethical dimensions of cellphilm use, dissemination, and archiving? These questions are taken up from interdisciplinary perspectives by established and new academic contributors from education, Indigenous studies, communication, film and media studies.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".