Comparing R&D tax regimes: Australia, Canada, UK and US
Bibliographic record
Abstract
This article does two things. It explores the definition, classification and categorisation of tax incentives, at a general level,\n and it compares how four global leaders target their R&D tax incentive regimes. The latter is an exercise in comparative\n tax law focusing on the R&D definition and the incentive’s operational details. It observes that the R&D definition is used\n by all of the regimes as a gateway to the relief and to curtail the scope of the relief by restricting R&D to "new scientific\n knowledge", where the US interprets "new" to mean new to the entity, whereas the UK, Australia and Canada, interpret\n "new" to mean new to the world. Thus, the US regime arguably focuses on creating and sustaining the competitiveness of\n its domestic firms by subsidising "knowledge acquisition" rather than "knowledge creation". It also observes that: whilst the\n US and Canada subsidise incremental improvements in new scientific knowledge, the UK and Australia do not, insisting\n instead on "substantial advances"; only the US uses an incremental credit, which arguably serves as a means of limiting\n the cost of this tax expenditure to the US Treasury, particularly given the broad scope of its R&D definition; none of the\n regimes respond to the consensus on the need for specialisation, adopting as they do a broad-brush approach to subsidising\n technological innovation; and by focusing on experimentation or scientific method, all of the regimes leave out a large area\n of potentially innovative and productive commercial activity (that is, commercial product development using an iterative or\n "trial and error" methodology).
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".