Bibliographic record
Abstract
Our knowledge of events and situations in the world plays a critical role in our ability to understand what is happening around us, to predict what might happen next, and to comprehend language. What has not been so clear is the form and structure of this knowledge, how it is learned, and how it is deployed in real time. Despite many important theoretical proposals, often using different terminology such as schemas, scripts, frames, and event knowledge, developing a model that addresses these three questions (the form, learning, and use of such knowledge) has remained an elusive challenge for decades. In this article, we present a connectionist model of event knowledge that attempts to fill this gap. From sequences of activities, the model learns both the internal structure of activities as well as the temporal structure that organizes activity sequences. The model simulates a wide range of human behaviors that have been argued to involve the use of event knowledge and the temporal structure of events. Furthermore, it makes testable predictions about behaviors not yet observed. Most importantly, the model's ability to learn event structure from experience is a novel solution to the question, "What is the form and representation of event knowledge?" (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".