Participatory Prototype Design: Developing a Sustainable Metadata Curation Workflow for Maternal Child Health Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract

 This paper describes the findings from a participatory prototype design project, where the authors worked with maternal and child health (MCH) researchers and stakeholders to develop a MCH metadata profile and sustainable curation workflow. This work led to the development of three prototypes: 1) a study catalogue hosted in Dataverse, 2) a metadata and research records repository hosted in REDCap and 3) a metadata harvesting tool/dashboard hosted within the Shiny RStudio environment. We present a brief overview of the methods used to develop the metadata profile, curation workflow and prototypes. Researchers and other stakeholders were participant-collaborators throughout the project. The participatory process involved a number of steps, including but not limited to: initial project design and grant writing; scoping and mapping existing practices, workflows and relevant metadata standards; creating the metadata profile; developing semi-automated and manual techniques to harvest and transform metadata; and end project sustainability/future planning. In this paper, we discuss the design process and project outcomes, limitations and benefits of the approach, and implications for researcher-oriented metadata and data curation initiatives.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.009 | 0.128 |
| Open science | 0.002 | 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 it