National Identity and the Limits of Platform Power in the Global Economy
Bibliographic record
Abstract
Abstract Among the defining features of the contemporary global economy are the digital disruption of economic sectors and the accompanying political and regulatory conflicts. Across the world, multinational technology firms have mobilized consumers as a key ally in these conflicts, a critical element of the platform power they wield. In this article, I examine how non-consumer identities can limit the exercise of platform power by such firms. By synthesizing the concept of platform power with research on political consumerism and national identity, I argue that activating national identity can generate opposition to policies favorable to multinational technology firms and, in turn, curtail their ability to appeal to public support. Empirically, this article uses an online, nationally representative survey fielded in Canada. I explore the determinants of support for global regulatory cooperation and the domestic policy status quo, as well as the causal effect of consumer and national identity framing using vignette experiments across three issue areas: banking, telecommunications, and taxation. The findings reveal that activating consumer identities consistently shifts support but the effect of national identity is more variable. This article thus contributes to scholarship on the digital economic transformation and the exercise of business power in the global economy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".