What can DDI do for you? An introduction to the DDI
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
Are you interested to learn about what DDI can do for your organization or institution? DDI is an international standard for describing data from the social, economic and behavioral sciences, currently moving into new fields. The standard contains metadata items that can be used to develop and document at different stages in the data lifecycle, from the first conceptualization through data collection, processing and dissemination and archiving. This tutorial provides an overview of the work products of the DDI Alliance. The conceptual basis of DDI will be described, introducing the participants to the main building blocks and items of the standard. Practical examples on how DDI can be used beneficially in the business processes of organizations and institutions that manage research data will also be shown. The overall approach of the tutorial is DDI-version agnostic. The examples shown will however be based on specific DDI versions (DDI-Codebook, DDI-Lifecycle). Main focus will be put on the following areas: Data description and variable management Questionnaire design and implementation Question and variable banking Making your data and metadata FAIR (Findable, Accessible, Interoperable and Reusable) using DDI Recorded version of this tutorial: https://youtu.be/RUleXvsOrGc
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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