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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".