Bibliographic record
Abstract
DDI has traditionally been deemed as the international metadata standard used in social, behavioural, economic, and health sciences; geared towards describing the data produced by surveys and other methods used in these fields. But should it be restricted to these disciplines? Should we only be teaching graduate students and researchers in these disciplines about DDI? During the summer of 2017, we developed a series of Research Data Management workshops teaching the basics of DDI to graduate students who are conducting research in the Ontario Agricultural College, University of Guelph. The workshops, delivered during the Fall 2017 semester, introduced best practices for capturing research metadata and research workflows to enable essential metadata capture throughout the research project. This presentation will discuss our approach to teaching DDI to the agricultural research community and the responses from the course participants. We will conclude our presentation by showcasing the Agri-environmental Research Data Repository.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.024 | 0.041 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.040 | 0.029 |
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".