FABRICATING STATELESS INCOME: DECONSTRUCTING THE DISCOURSES OF MULTINATIONAL PLATFORM CORPORATIONS’ TAX AVOIDANCE STRATEGIES IN AUSTRALIA AND CANADA
Bibliographic record
Abstract
While upending domestic and global news media’s advertising income streams via the provision of superior “commercial datafication” (Mansell, 2021) and “digital tracking and profiling” (Christl, 2017) functionalities, Multinational Platform Corporations (MNPCs) have also implemented opaque strategies to minimize their domestic and global tax liabilities. These strategies have been an important contributor to both Meta and Alphabet’s surging profits over the past decade – from US$1.5b for Meta in 2013, to $39b in 2023 and, for Google, now listed as part of parent company Alphabet, from a US$12.73b profit in 2013 to US$74b in 2023. These spectacular levels of profitability have been achieved despite a proliferation of local and some transnational ‘diverted profits tax’ laws (Colbran, E., & Farhat, S, 2023; Dunne, 2016) designed to prevent companies from shifting revenue from high-tax countries to ‘low or no tax’ jurisdictions. MNPCs have been able to not just continue to transform the majority of their revenue into "stateless” income (Kleinbard, 2016), which is not taxed in the country where the income is generated nor, often, even in the country where the corporation is headquartered, but to get better at this and other forms of tax avoidance, via improving both their ability to strategically “navigate institutional complexity” (Kornelakis, A., & Hublart, P. (2022) and, as this paper argues, to shape national and transnational discourses about their strategies. In both Canada and Australia, these strategies continue to allow the deflecting of public and political pressure to support (or mitigate the disruption to) local news ecosystems.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".