Exploring the Ethics of the Participant-Produced Archive: The Complexities of Dissemination
Bibliographic record
Abstract
Cellphilm methodology is a process where research participants create short cellphone videos in an effort to move toward social change. Cellphilms can be disseminated across physical spaces (eg. through sharing phone-to-phone and through screenings) and digital spaces (eg. by uploadig to social media sites). This chapter focuses on the use of participatory digital archives, such as YouTube, as a means for teachers to view and review their cellphilms to encourage reflection on teacher identity and the use of cellphilms as an educational tool. We see cellphilming as an emerging participatory research methodology and its integration with online participatory digital archives holds both promise and challenges. As such, our chapter explores the development of a researcher/participant collaborative cellphilm archive in a project with pre-service social studies teachers at the University of Prince Edward Island in Canada. We discuss some of the ethical issues that are associated with relying on YouTube as a digital archival space when conducting visual participatory research with pre-service teachers. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.110 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.089 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".