Bibliographic record
Abstract
5G has the potential to expand the horizons of digital inclusion by providing higher speeds, lower latency, and support for more devices on a given network. However, mis‐ and disinformation about 5G has proliferated in recent years and stands to be a persistent barrier to the adoption of this generation of wireless technologies. After rumours linking 5G to Covid‐19 emerged in the wake of the pandemic, isolated actors attempted to disrupt infrastructure with a perceived connection to 5G. Media coverage of these incidents inadvertently spread such claims, engendering lasting uncertainty about 5G. Infrastructure scholars have long held to the maxim that “the normally invisible quality of working infrastructure becomes visible when it breaks” (Star, 1999, p. 482), but efforts to interpret the uptake of mis‐ and disinformation have struggled to define the technical difference 5G makes and describe diffused acts of anti‐5G sentiment that exploited its slippery symbolic associations. What broke to make 5G so visible? This article reassesses interference with infrastructure through the lens of a literary metaphor derived from Miguel de Cervantes’ epic novel Don Quixote. Using the Don’s famed joust with windmills, I examine what efforts to disrupt the development of 5G in 2020 can tell us about infrastructural transition. With reference to Quixote’s tilt, I contend that the disruptions of 2020 illustrate conflicting imperatives of inclusion and exclusion underlying neoliberal schemes of telecommunication development.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".