To the Cloud: Big Data in a Turbulent World by Vincent Mosco
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vincent Mosco begins and ends To The Cloud: Big Data in a Turbulent World by exploring metaphors about clouds and applying them to cloud computing. These metaphors offer a way into understanding the history of cloud computing: where it came from, why it began, how its evolved, and the ways it works in our everyday lives. He draws on literature, including a book entitled The Cloud of Unknowing by a medieval English monk (pg. 13). As I write this, I switch over to my streaming music service momentarily and discover it playing a song of the same name, this time by a contemporary artist, James Blackshaw. Given that I’d heard of neither the song nor artist until this very moment, this makes me a bit suspicious about how closely I’m being watched by my music player. Was it reading my email? Did it discover my notes, uploaded to the cloud on Evernote? Does it know this book was shipped to me? It’s almost difficult to believe it is complete coincidence. And yet this is one of the promises of the cloud and big data - a world where what we want (even when we didn’t know we wanted it) is at our finger tips exactly when we want it.
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.
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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.011 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 it