Bibliographic record
Abstract
Session topic: What makes great data documentation?Documentation is the tool that describes how and why a database was created, what its strengths and limitations are, and how all of the various components fit together. As such, it is an invaluable resource for helping others understand what they can do with the data. Please join us for a discussion of what makes great data documentation. This session will begin with a 40-minute integrated presentation by the Manitoba Centre for Health Policy (MCHP) and the Institute for Clinical Evaluative Sciences (ICES), two of the leading population data research centres in Canada, covering the following topics: 1 – Structured Overviews 2 – Data Models 3 – Data Dictionaries 4 – Other Documentation and Published Reports 5 – Integrating Blog or Analyst Notes 6 – Data Quality Reporting VIMO tables Heat maps Trend analysis Relevancy Session Facilitators:Mahmoud Azimaee, Institute for Clinical Evaluative Sciences (ICES) Mark Smith, Manitoba Centre for Health Policy (MCHP) The Intended Outcome:A research paper based on the discussion for publication in the International Journal of Population Data Science (IJPDS). All participants are invited to join us as co-authors in drafting and revising the paper.
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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 | 0.134 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.024 | 0.039 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.080 | 0.047 |
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".