Automating the production of descriptive tables at Statistics Canada: mog.ado, a user-written program
Bibliographic record
Abstract
Research at Canadian Social Trends within Statistics Canada, Canada's premiere Statistical agency, often involves the creation and analysis of numerous descriptive tables. These tables provide convenient and easy-to-understand information for the general public, one of our many clients. Analysis generally requires an understanding of what estimates are statistically different from each other. Statistics Canada's quality control measures require that any released estimates pass reliability and confidentiality standards. Both of these needs are often operationalized by numerous lines of Stata code after the use of a command, such as mean. This presentation is about a user-designed program, mog, that is essentially a front-end for the mean and test commands. It produces a fixed-width table of means over the groups specified. This table can then be easily copied into other productivity tools (Word, Excel, Open Office Apps, etc.) for any additional formatting and publication. The key is that the results are tabular and can copy properly as a table, significance tests of estimates versus a reference group are already performed and indicated, and quality control symbols indicating minimum sample size and individual significance are shown. I plan to present the amount of code to perform the tasks the old way, and thus time saved using the command, as well as the many options it has.
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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.186 | 0.097 |
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".