Using Network Science to Measure Centrality and Standardness in Event Knowledge
Bibliographic record
Abstract
An important issue in event cognition concerns how activities come to mind when people think about events (eat at a restaurant). Linear theories suggest that people think of activities in a temporally linear order, whereas hierarchical theories suggest that activities come to mind based on their centrality (i.e., importance). The current study used five network science centrality measures (CheiRank, PageRank, 2D Rank, Betweenness, and Closeness) derived from 80 temporally structured event networks to predict participants’ centrality and standardness rankings and ratings. Participants were provided with 40 events and 4-10 activities per event, and ranked or rated each activity’s centrality or standardness. Linear mixed-effect regression showed that CheiRank, which assigns importance to activities that have many influential outgoing links, was the strongest predictor. This suggests that people’s understanding of centrality relates to the degree to which an activity leads to other activities, supporting hierarchical models and the Event Horizon Model.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".