MOBILE MEDIA DURING THE PANDEMIC: FOUR SCENARIOS TO HELP US IMAGINE A MOBILE MEDIA FUTURE TOWARDS LIBERATION
Bibliographic record
Abstract
Together, our projects pose the question of what else can be possible as a way to rearrange the power relations that have contributed to the asymmetric flows of information and resources to some instead of others. We are inspired by indigenous scholars’ claim that “decolonization is not a metaphor” because liberation should not be a metaphor: it should be a possibility. We, panelists, hope to engage conversations that address the future of the internet, especially the future of mobile communication with the lessons learned from studies. Panelists offer a close reading of four scenarios: in South Korea among residents whose locative data tell a story about their comings and goings, among International Exchange Students mobile media use during the on-set of travel bans, in China among rural-to-urban female migrant workers, and in Brazil among those who used the Unified Slum Dashboard to called attention for proper government intervention. Among our research methods are interviews, observations, content analysis, and case study to bring attention to and make institutional space for voices and accounts of community engagement that have been marginalized or overlooked. Our findings share a common theme that mobile media simultaneously can liberate and complicate our mobility choices, especially during a global pandemic, but that it can be a civic media in which liberation can be possible through more careful policies that take minoritized experiences into consideration for future policies.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.006 |
| 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".