WHAT DEAD-AND-DYING PLATFORMS DO FOR INTERNET STUDIES: SITUATING TECHNOLOGICAL FAILURE, DIGITAL AFTERLIFE, AND THE WEB THAT WAS
Bibliographic record
Abstract
This panel explores internet histories through the lens of “platform death” as a way of understanding how digital communities grapple with technological failure, power dynamics, and the divergent notions of the digital afterlife. Collectively, the contributions address the cultural, geopolitical, economic, and socio-legal repercussions of what happens when various platforms fail, decline, or expire. We bring together five presentations that draw on different methods—including document analysis, semi-structured interviews, participant observation—to explore the frailty of platforms, their underlying infrastructures, and their trace data. Together, by examining and theoretically situating the histories of five different platforms (TroopTube, Fanfou, MySpace, YikYak, and Couchsurfing), we consider and complicate how the concept of “platform death” as a metaphor can help reveal the Web’s rhythmic temporality, digital media’s constant reinvention of forms, and the collision of hegemonic and fragile infrastructures in divergent cultural contexts. We ask: What are the theoretical implications of situating platforms as killable, ephemeral, precarious, or transient technologies? What—and who—kills platforms, and in what ways can they have uncertain digital afterlives and even resurrections? What can conceptualizations of dead and dying technologies tell us about the Internet’s growth and stagnation, its present and futures? What is (un)knowable about platforms that once were, and how can this knowledge inform our predictions of future technological failure? We aim to build community, collective imaginings, and future collaborations around a research agenda that centers mnemonic experimentation, comparative platform studies, and archival contestations.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".