Bibliographic record
Abstract
An exciting science fiction collection that looks at what future communication might look like and how our shifting relationships with technology could change this most human of capabilities. In Communications Breakdown, award-winning editor Jonathan Strahan asks some of the world's best science fiction writers to consider how the very idea of communication might change in the future. Rich terrain for speculation, this anthology brims with human stories about the future face of our age-old need to connect. As cyberpunk pioneer William Gibson said, “The future is already here—it's just not evenly distributed.” So what happens when inequalities keep the future from everyone's front door? Who is in control? These stories show humanity's ability to construct the best possible worlds while also battling our potential to inflict unlimited harm. Communications Breakdown features contributions from Canadian Science Fiction and Fantasy Hall of Famer Cory Doctorow, the winner of the Times of India AutHer Award Lavanya Lakshminarayan, Hugo Award winner Ian McDonald, as well as an interview with digital privacy activist Chris Gilliard by author and journalist Tim Maughan. Breaking down how we think about communication, Communications Breakdown calls readers to look at how vulnerable our modes of communication—and indeed, we ourselves—are. Contributors Elizabeth Bear, S.B. Divya, Cory Doctorow, Chris Gilliard, Lavanya Lakshminarayan, Ken Macleod, Tim Maughan, Ian McDonald, Anil Menon, Premee Mohamed, and Shiv Ramdas. Artwork by Ashley Mackenzie
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.340 | 0.156 |
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".