Detecting the Magnitude of Events from News Articles
Bibliographic record
Abstract
Forced migration is increasingly becoming a global issue of concern. In this paper, we present an effective model of targeted event detection, as an essential step towards the forced migration detection problem. To date, most of the the approaches deal with the event detection in a general setting with the main objective of detecting the presence or onset of an event. However, we focus on analyzing the magnitude of a given event from a collection of text documents such as news articles from multiple sources. We use violence as an illustration as it is one of the most critical factors of forced migration. The recent advancements in semantic similarity measures are adopted to obtain relevant violence scores for each word in the vocabulary of news articles in an unsupervised manner. The resulting scores are then used to compute the average daily violence scores over a period of three months. Evaluation of the proposed model against a manually annotated data set yields a Pearson's correlation of 0.8. We also include a case study exploring the relationship between violence and key events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".