Flying the skies to wire the seas: Subsea cables, remote work, and the social fabric of a media industry
Bibliographic record
Abstract
Since the commercial aviation boom in the 1960s and 70s, the subsea cable industry has relied on global air travel for network development, infrastructure maintenance, and market penetration. However, COVID-19 disruptions forced a shift to remote work, challenging traditional travel practices and presenting an opportunity for carbon emission reduction. This study investigates the industry’s response to the “new normal” and its implications for mobility and sustainability. We employ a media industries approach and conduct open-ended interviews with industry leaders to examine the potential balance between remote work benefits and essential in-person aspects, questioning whether the industry should return to pre-pandemic travel levels or embrace remote work’s ecological and financial benefits. Our findings indicate that remote work suitability varies depending on project stage, involved personnel, and the existing social fabric. To facilitate travel-related carbon footprint monitoring for cable consortiums, we developed a calculator to determine the industry’s emissions when adopting remote work. Our interdisciplinary study also emphasizes mobility’s intricate role in subsea cable systems and broader media infrastructure studies. By scrutinizing corporate cultures, communication practices, and transportation infrastructures, we enhance the scholarly comprehension of the social fabric underpinning global digital networks and investigate potential shifts toward a more sustainable media industry.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".