Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
Bibliographic record
Abstract
The present data article describes high-school drop-out related web activities in Canada, from 2004 to 2012, obtained mining Google Trends (GT), using high-school drop-out as key-word. The searches volumes were processed, correlated and cross-correlated with statistical data obtained at national and province level and broken down for gender. Further, an autoregressive moving-average (ARMA) model was used to model the GT-generated data. From a qualitative point of view, GT-generated relative search volumes (RSVs) reflect the decrease in drop-out rate. The peak in the Internet-related activities occurs in 2004 (56.35%, normalized value), and gradually declines to 40.59% (normalized value) in 2007. After, it remains substantially stable until 2012 (40.32%, normalized value). From a quantitative standpoint, the correlations between Canadian high-school drop-out rate and GT-generated RSVs in the study period (2004-2012) were statistically significant both using the drop-out rate for academic year and the 3-years moving average. Examining the data broken down by gender, the correlations were higher and statistically significant in males than in females. GT-based data for drop-out resulted best modeled by an ARMA(1,0) model. Considering the cross correlation of Canadian regions, all of them resulted statistically significant at lag 0, apart from for New Brunswick, Newfoundland and Labrador and the Prince Edward island. A number or cross-correlations resulted statistically significant also at lag -1 (namely, Alberta, Manitoba, New Brunswick and Saskatchewan).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".