Dataset and figures on time-series analysis of child restraint policy impact in Chile
Bibliographic record
Abstract
The main objective of this data article is to present the data set which depicts the impact of child restraint legislation in Chile and its regions. The population of the study consisted of all car crashes records provided by the national police from 2002 to 2014, which included children aged 0-3. Auto Regressive Integrated Moving Average ARIMA and Poisson model were used to present the association between the dependent and independent variables of interest. When the data are analyzed, it will help to determine the degree of relationship and the strength of significance between child restraint legislation policies enacted in 2005 and 2007, and child occupant fatalities and injuries. The data are related to "Impact of child restraint policies on child occupant fatalities and injuries in Chile and its regions: An interrupted time-series study" (Nazif-Munoz et al., 2018).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".