Bibliographic record
Abstract
The cloud is an utterly ubiquitous Wi-Fi and enormous storage system that runs on the internet, and internet users are consuming this service when they use iCloud, Dropbox, Netflix, Flickr, Google Drive, and many more, without even feeling its existence. Storing something in the cloud actually means storing your data in a real, physical server. Tung-Hui Hu in his new book, A Prehistory of the Cloud, illustrates the inconsistency between the cloud’s airy image and its physical being and how it is virtual and real at the same time. In A Prehistory of the Cloud, Hu depicts the cloud through a poetic image, saying ‘… the cloud is silent, in the background, and almost unnoticeable’. It is a reliable piece of infrastructure unless something goes wrong, such as when a cyberattack happens or a dictator wants to switch off power. The cloud, as Hu explains, is an example of what computer scientists call visualization—that is, a method for transforming real things into rational objects; the thing(s) being transformed could be a server full of data turned into a ‘cloud drive’ or a physical network turned into an icon of a cloud. However, in our understanding of the cloud, the gap between the virtual and the real still remains. In his book, Hu examines what happens in the gap between the virtual and the real.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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 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".