METHODS THAT MOVE US: CREATIVITY AND ETHICS IN RESEARCHING DIGITAL YOUTH CULTURES
Bibliographic record
Abstract
For scholars exploring digital youth cultures, creative research methods offer the potential to disrupt existing power imbalances, form empowering creative practices or closely engage with knowledge production that is dynamic, embodied and socially contextual. Yet the experience of doing creative research methods poses challenges that are often under- or unacknowledged in our work.
 This roundtable brings together researchers from different countries (Australia, Canada, Denmark, and the United Kingdom), career stages, and with differing methodological expertise to discuss the ethical, personal, theoretical and methodological challenges of creative methods research to understand intersectionally diverse groups of young people and their digitally lives.
 In the spirit of creative methods, our roundtable opens with a short creative, hands-on task that encourages reflection on the following aspects of researching young people’s digital lives: approaching ethics, anonymity, care and vulnerability; choosing suitable research methods; including young people in research; rethinking what counts as “data”; and publishing research results.
 We will then share brief summaries of our research, addressing ethical challenges at different stages of the research process; from diversity in recruitment and interactions between research designs and young people’s digital lives, to representations of young voices in academic writing and young people’s possibilities of long-term commitments in participatory research. We want to emphasize the importance of openly discussing the challenges we face as researchers but also as practitioners, educators and activists, and we will invite participants to discuss their own research relating to creative methods, ethics and youth digital cultures.
 Our work emphasises the political and pedagogical need for research that approaches young people’s diverse media practices through respectful listening and co-production methods. Additionally, we wish to reflect on our own positions as researchers and feminists, and on which perspectives we can represent and which we cannot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".