Practical documentation of qualifying research activities for the SRED tax credit
Bibliographic record
Abstract
The Canadian tax program for Scientific Research and Experimental Development (SR&ED) is a crucial component of Canadian economic policy as it encourages domestic companies to engage in risk-taking initiatives through tax incentives such as credits. While the SR&ED program has evolved over the decades, the SR&ED Five Questions have established the principal criteria that determine the eligibility of an SR&ED claim. The key take-away points of this paper are that the Canada Revenue Agency is progressively becoming more particular and stringent when reviewing SR&ED claims and in light of this trend, documentation is becoming ever important in the substantiation of SR&ED claims. Although not required with submission, documentation provides taxpayers with evidence to support their claims in the case of a CRA or court challenge. In the event of an audit, a taxpayer must prove that its SR&ED activities are eligible and meets the “Five Question Test.” As this paper argues, recent case law has shown the importance of clear and relevant supporting evidence, especially documentation, in substantiating that an SR&ED project meets the Five Questions criteria. Key words: Tax, taxation, research and development, accounting, incentive, tax credit, canada, scientific research and experimental development.
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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 | 0.213 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".