Best Practices Documents - Are They Really Necessary?
Bibliographic record
Abstract
In Ontario, there is a movement afoot to mark up surveys in DDI and put them in an interface that allows them to be shared with other universities. A noble exercise, indeed! Our project, (Ontario Data Documentation, Extraction Service and Infrastructure Initiative) provides university researchers with unprecedented access to a significant number of datasets in a web-based data extraction system. Access to the data with its accompanying standardized metadata is key to our project. However, the staff marking up these surveys do not necessarily think alike, so the formats used in marking up the surveys can and do vary across institutions. And this is taking place in only one province so this begs the question of what the formatting looks like when the marking up is done nationally. In this presentation, we will discuss the five Ws of a Best Practices Document: why we need one; when it happened; where it was put together; what the process was; and who will benefit from it.
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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 | 0.199 | 0.361 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.050 | 0.058 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.022 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".