WHAT DEAD-AND-DYING PLATFORMS DO FOR INTERNET STUDIES: SITUATING TECHNOLOGICAL FAILURE, DIGITAL AFTERLIFE, AND THE WEB THAT WAS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it