Shaping infrastructural futures: The International Telecommunication Union’s visions for mobile communications and the anticipatory politics of 5G standardization
Bibliographic record
Abstract
This article shows how dominant actors inscribe certain ideas, visions, and predictions of infrastructural futures for international mobile telecommunications through standardization. It argues that standard setting is a key avenue that brings different (and sometimes divergent) interests, groups, concerns, and activities into alignment around a certain vision of social and technological progress. To demonstrate this, two key stages in the 5G standardization process were examined. First, we explored the path to the release of IMT-2020—the standard for 5G networks, devices, and services released by the Radiocommunication Sector of the International Telecommunication Union. Through the standard setting process, two key visions of 5G—one “evolutionary”, the other “revolutionary”—became highly influential ideas of a future worth striving for. Second, we examined how one technical feature of the IMT-2020 standard—the capacity for network slicing—was realized through the work of partner organization the Third Generation Partnership Project (3GPP). In doing so, this article reveals the processes that define the infrastructural conditions that underpin international mobile telecommunications. It also draws attention to how standardization has the potential to redefine the parameters of mobile media and communication in significant ways.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".