Bibliographic record
Abstract
In this paper, I extend the notion of franchise nations, borrowed from Neal Stephenson’s cyberpunk novel Snow Crash (1993), in order to employ it as a device for thinking about the future of the nation. I argue the concept to be particularly well suited for such contemplation because of its sound grounding in the historical intermesh of economic, political and cultural motivations intrinsic to the concept as well as lived experience of the nation. I illustrate this very briefly by casting (mainland) China as the master franchisor and the overseas Chinese as franchisees. Specifically, I discuss the media events concerning China that took place during 2008, such as the protests and counter-protests that occurred at various legs of the Olympic Torch Relay, the Sichuan earthquake of 12 May and the opening ceremony of the Beijing Olympics on 8 August, and reactions to these happenings from overseas Chinese located variously in Australia, Canada and the United States. I argue that employing the notion of franchise nations lays bare the commercial and political instrumentalism behind the promotion and courtship of diasporas by home nations but, crucially, also aids in the understanding of the reciprocal processes by which franchisees are fashioned out of these communities. Finally, I suggest that, aside from China, franchise nations may also be a useful approach for thinking about how nations like India and Singapore are expanded, exported and explained into the future.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| 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".