Participatory Prototype Design: Developing a Sustainable Metadata Curation Workflow for Maternal Child Health Research
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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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 | 0.074 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".