Making global oceans governance in/visible with Smart Earth: The case of Global Fishing Watch
Bibliographic record
Abstract
The number and variety of technologies used for environmental surveillance is expanding rapidly, making constant data collection and near ‘real time’ analyses possible. ‘Smart Earth’ describes networked infrastructures comprised of devices and equipment and signals to the human dimensions inherent to developing, deploying and putting technology and large datasets to use. In this paper, we situate Smart Earth in terms of technological products and human practices and consider the relationship between Smart Earth and global environmental governance. Specifically, we review emerging literature and present a case study of an organization founded by environmental non-profit, SkyTruth, tech industry behemoth, Google and marine conservation NGO, Oceana. Called ‘Global Fishing Watch’ (GFW), this organization builds geospatial datasets, hosts an online mapping platform where anyone with internet access can surveil various types of ocean-going vessels and shares data and map products with scientists and practitioners. Two critical points emerge through the case. First, we show that GFW expands its surveillance capacity by pursuing ‘data sharing’ partnerships with sovereign states, many in the Global South. Second, the maps and datasets produced by GFW link vessels to a ‘flag state’ while the firms, subsidiaries and financiers that may own and/or operate these vessels remain obscure – and hence so too does the political economy of oceans fisheries. GFW maps and datasets offer new approaches to tracking fishing and are advancing fisheries science. At the same time, they rely on and are only legible through hegemonic geopolitical and political–economic orders deeply implicated in industrial (over)fishing. The norms and domains of global environmental governance are expanding, but Smart Earth ‘solutions’ risk leaving the structural drivers of environmental change unaddressed.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".