Bibliographic record
Abstract
Abstract What do you do when you’re not asleep and when you’re not eating? You’re most likely waiting—to finish work, to get home, to finish your chores. This book is not really about how to manage all that waiting—“an action,” according to the OED, “of staying where one is until a particular time or event.” It’s a book describing how many people experience waiting. Waiting, which is sculpted by the passing of time, is an experience just as much as it is a situation. In this book I’ll be focusing on the experience, on how it feels to wait. This experience can encompass such things as hesitation and curiosity, dithering and procrastination, hunting and being hunted, fearing and being feared, dread and illness, courting and parenting, anticipation and excitement, listening to and even performing music, being religious, being happy or unhappy, being bored and being boring, doing business and making decisions (all of which I’ll discuss). Waiting is also characterized by such brain chemicals as serotonin and dopamine. They enable the experience of waiting and they can even change the way that waiting’s basis, the passing of time, is registered. Waiting, probably the most commonly experienced situation that humans and animals encounter apart from sleep, is the experience that may characterize most interpersonal relations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.785 | 0.640 |
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".