BodyData : a modular system for the design and implementation of complex multistep experiments
Bibliographic record
Abstract
In this thesis, we address the challenge of acquiring high-quality measurement data from real-world experiments. Experiments with human participants can be expensive, both in terms of scheduling participants as well as equipment requirements. Because of these constraints limiting data collection, we desire software tools for data quality assurances that are active during each measurement session. We propose a modular approach to conducting experiments based on the inputs, outputs, and dependencies between individual data-generating operations that we call measurement services. Formally defining the output of each operation provides clear quality assurance targets to aim for during the experiment session. Our framework of modular components also emphasizes extensibility and reusability in the development of new experiments. We implemented our approach by developing BodyData, a web application-centered system designed to measure, store, and securely access data from experiments with human participants. BodyData was tested in our lab; two case studies are presented to illustrate the utility of the system in practice. We discuss how we provide improved quality assurance through the use of configurable data entry constraints as well as visual feedback during the measurement session. We also discuss how we support queries from authorized clients for use in analysis and visualization of stored data.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".