Bibliographic record
Abstract
In January 1998 a massive ice storm descended on New York, New England, and eastern Canada. It crushed power grids from the Great Lakes to the North Atlantic, forcing thousands of people into public shelters and leaving millions of others in their homes without electricity. In this riveting book Stephen Doheny-Farina presents an insider’s account of these events, describing the destruction of the electric network in his own village and the emergence of the face-to-face interactions that took its place. His stories examine the impact of electronic communications on community, illuminating the relationship between electronic and human connections and between networks and neighborhoods, and exploring why and how media portrayals of disasters can distort authentic experience. Doheny-Farina begins by discussing the disaster and tracing the origins of the storm. He then goes back two hundred years to tell how this particular electric grid was built, showing us the sacrifices people made to create the grids that (usually) connect us to one another. Today’s power grid, says Doheny-Farina, has become more vulnerable than we realize, as demand begins to outstrip capacity in urban centers around the nation. His book reminds us what those grids meanboth positively and negativelyto our electronically saturated lives.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".