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.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.019 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".