DDI Publishing: A Hands-on Workshop
Bibliographic record
Abstract
This workshop will provide hands-on experience in marking up a Statistics Canada dataset using Nesstar Publisher. This workshop is Part 3 of a continuing series on Understanding DDI (Data Documentation Initiative). Nesstar Publisher is a software/hardware product which allows users to markup documents in XML (EXtensible Markup Language) using DDI specifications. A brief overview will act as a refresher to the DDI specifications before the exercise begins. The exercise will provide users with a start to finish example of creating a DDI compliant dataset. Participants will each be provided with access to a Statistics Canada dataset, a workbook and access to Nesstar Publisher. With guidance from the instructors, participants will learn how to create a unique template, fill in tags and publish the dataset onto a Nesstar server. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-218.)
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.112 | 0.096 |
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".