A Guide to the Canadian T3010 for Users of the U.S. Form 990
Bibliographic record
Abstract
This research note introduces nonprofit researchers accustomed to the U.S. Form 990 to the Canadian data captured on the T3010 financial form that will soon be available to researchers on a broad scale. Similar to the Internal Revenue Service (IRS) Form 990, the Canada Revenue Agency (CRA) T3010 is an annual information filing required of every Canadian charity that meets certain requirements. However, several elements in the data are unique to the Canadian context, while others are similar to the Form 990 but must be interpreted with attention to differences in definition and accounting practice that might otherwise complicate attempts at cross-national comparisons. Once these elements and the data’s limitations are understood, however, the forthcoming datasets will allow rich analysis for researchers and practitioners in areas that are yet unexplored with large data sources.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.355 | 0.311 |
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".