Seeing the Forest from the Trees: A Novel Deep Learning-Driven Aggregate Embedding for Group-Level Analysis of Public Health Data
Bibliographic record
Abstract
In the years since the COMPASS dataset initiative was begun, many important research questions have been investigated using its large amount of health information pertaining to high school students across Canada, with findings guiding many decisions made by policy makers [1]. However, to use traditional statistical methods, specific data points must be selected by researchers to include in the analysis, leading to possible unexpected relationships and connections across the study's 280 data points being missed. As well, most analysis is done on a per-student basis, while policies are often implemented at the school level, so understanding behaviours across a school's population can make it easier for school decision makers to interpret findings. Motivated by these goals, this study introduces a novel deep learning-driven aggregate embedding method to determine group-level representations for individual schools from student-level survey responses based on architecture introduced in Variational Autoencoders [2]. This study aims to produce a method which allows for new patterns to be identified in the COMPASS data and for the resulting embedded representations to be applied in future analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".