Bibliographic record
Abstract
Abstract It has been asked, ‘‘You know the sound of two hands clapping; what is the sound of one hand clapping?’’ Of course one hand does not clap. But there are answers to a related question, ‘‘You know the sound of two hands clapping; what is the sound of manyhands clapping?’’ Zolten Neda and his physicist colleagues in Romania have studied this question extensively in concert halls, theaters and opera houses (Neda et al. 2000). One of their conclusions is that at some point during the applause the aggregate noise from hand clapping will be periodic. Probably, most of us have experienced this phenomenon. (Being physicists, Neda and his colleagues also learn a lot of other things that the casual observer might miss.) This periodicityof the applause is an example of synchronization. Each component system— each pair of hands—acts in concert with all the other component systems to create a uniform pattern of clapping. But the applause may not neces sarilystayperiodic. If some audience members are more enthusiastic than others and break the pattern by clapping more frequently, then the applause can again become chaotic. Thus the degree of synchronization seems to depend upon the audience members being mutuallycoupled bya common level of enthusiasm. As an example, Neda et al. note, historically, that clapping at now-defunct communist party rallies typically stayed in the rhythmic state. One might conclude that the level of enthusiasm was fairly low at such gatherings. But let us discuss pendulums.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 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".