COLLABORATING AT MICROSCOPIC AND MASSIVE SCALES: THE CHALLENGE AND VALUE OF COVID ISOLATION FOR CRITICAL INTERNET STUDIES
Bibliographic record
Abstract
Global events like a pandemic or climate change are massive in scope but experienced at the local, lived, microscopic level. What sorts of methodologies and mindsets can help critical internet researchers, functioning as interventionists or activists, find traction by oscillating between these levels? How can we push (further) against the boundaries of research methods to build stronger coalitions and more impactful outcomes for social change among groups of scholars/researchers? This panel presents four papers addressing these questions based on a large scale online autoethnography in 2020. This “Massive/Micro” project simultaneously used and studied the angst and novelty of isolation during a pandemic, activating researchers, activists, and artists to explore the massive yet microscopic properties of COVID-19 as a “glocal” phenomenon. The challenge? Working independently and microscopically through intense focus on the Self but also working with distributed, largely unknown collaborators, in multiple platforms. The emerging shape of the project itself showcases the challenges and possibilities of how research projects at scale can (or don’t) reflect and build social movements. The panel’s four papers situate the project through a kaleidoscope of perspectives featuring participants from 7 countries, who variously explore: the value of the project for precarious or early career researchers, how MMS worked as both collaborative space and critical pedagogy, how non-institutional or playful experimentation in asynchronous collaborations can lead to new synergies; and how MMS developed an independent life of its own, beyond studying COVID to generating multiple communities of future digital research practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".