Estimation of Bullying Incidence Using Linear Regression Algorithm
Bibliographic record
Abstract
Bullying is a violent act intended to cause harm or humiliation to another person. Bullying can take many different forms, including verbal and nonverbal, and it frequently targets people who are thought to be weaker or different. Nevertheless, because many bullying incidents go unreported, it is challenging to gather reliable statistics on the prevalence of bullying. In this study, the number of bullying cases in the field of education is estimated using a linear regression approach. This algorithm is used because it may estimate based on pertinent data, like the gender-based type of bullying and data on the quantity of bullying events that occurred in the preceding year. According to the study's findings, 2,250 bullying incidences are predicted for the upcoming year 2024, with a MAPE (Mean Absolute Percentage Error) of 0,07% or an accuracy level of 99,3%, categorized as highly accurate forecasting results.
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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 | 0.000 |
| 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) | 0.000 | 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".