Bibliographic record
Abstract
We consider three graph-theoretical models of spread and competition motivated by biological applications and also by spread of opinions in social networks and fires in geographical areas. We modify the voter model of Clifford, Sudbury, Holley, and Liggett by introducing confidence levels. In the voter model, a group of voters has opinions “yes” or “no, ” interpreted as infected or non-infected in a disease application. As time progresses each voter’s opinion is influenced by his or her neighbors, with the confidence level of a voter determining how quickly the voter reconsiders his or her opinion (how quickly a person might change disease state). We show that the voter model with confidence levels always results in a uniform opinion, and we determine the probability of each uniform opinion based on the initial opinions and–what is unusual in this subject–on the structure of the underlying graph. We also consider a perfectly contagious disease, where vertices adjacent to infected vertices become infected at every discrete time step. The only intervention allowed is a limited number of vaccinations per time step. This model of disease spread is equivalent to a model of fire spread introduced by Hartnell where firefighters correspond to vaccinations.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".