Bibliographic record
Abstract
More than a decade ago, when I was living in Oakland, California, I was under the impression that if one found a book lying on the street or stacked in a free pile near the sidewalk, one should probably read it because it might carry a message.Indeed, I did so when I found the book The Captive Mind, by Czeslaw Milosz (1953), around the end of the last century, in a red jacket, abandoned on the sidewalk.In the book, Milosz describes the fate of "human materials," and this idea stuck with me through the end of graduate school, all sorts of life events, the early years of teaching, and the eight years it's taken me to write this book.Under particular conditions and certain systems, Milosz seems to say, humans function as both materials and living beings or as subjects and objects.My first book was about the constraint of human materials.This is a book about human materials also.The archive I'm calling the "database of dreams" was at its core a collection of just such materials-sometimes also called by their collectors "human documents"-and was part of a larger movement to collect the same.In the process of telling the story of the archive housing these documents and the lives they represent, I have incurred many human debts and drawn on many friendships.First are the people from around the world who contributed to the archive I've written about.These are the subjects whose stories and dreams I have referred to, drawn from, or retold in fragmented fashion.Most are deceased; many may not have known that their dreams or other materials were being stored in a social-scientific
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.412 | 0.264 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".