The DDI Variable Cascade: Describing Data to Optimize Reusability and Comparison
Bibliographic record
Abstract
The first webinar in the 2023 CODATA – DDI Alliance webinar series, ‘The DDI Variable Cascade: Describing Data to Optimize Reusability and Comparison’, took place on 9 March 2023. In the social, behavioural, and economic sciences, data is often described as sets of ‘variables’ – essentially the columns in a table or the answers, respondent-by-respondent, to a question in a survey. The term ‘variable’ is employed by researchers and data managers to describe a range of specific uses of this granular concept, often in ways that lack sufficient clarity to support automation. The DDI variable cascade is a more nuanced model which describes the stages of a variable from conception to its use in a data set. Drawing on other conceptual standards such as the Generic Statistical Information Model (GSIM), DDI provides an implementation mechanism for the increasingly common granular management and reuse of data. In this webinar, the model behind the variable cascade will be presented, along with the practical implementation of the various types. Presenters are Arofan Gregory (consultant, DDI Alliance and CODATA), Hilde Orten (Sikt, the Norwegian Agency for Shared Services in Education and Research) and Kathryn Lavender (US National Archive of Computerized Data on Aging [NACDA]), with a welcome by Laura Molloy (CODATA).
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 | 0.138 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".