Acceptance and Leadership--Hegemonies of E-Commerce Policy Perspectives
Bibliographic record
Abstract
This paper presents an analysis of the e-Commerce policies developed and implemented in the USA, Canada, Australia, Victoria, Finland, Norway, the UK, Ireland, the EU (by the OECD), Singapore, Japan, Malaysia, Thailand and Hong Kong (Special Administrative Region). The paper shows that e-Commerce policy adopted is generally trying to achieve two fundamental aims: 1. to minimize regulatory environments for e-Commerce; and 2. to ease logistical problems in doing e-Commerce - i.e. in paying electronically, in delivery of goods and in customs, tariffs and duties. These strategies are designed to create an environment where e-Commerce is adopted by business and government in these countries to achieve 'best practice', to become 'modern', to gain 'efficiencies', because 'it is the way to go', because 'we must have it, because everybody has it', and because they 'perceive the benefits of it'. In essence it is being used to gain hegemony in the economic competitiveness of the geopolitical environment created by the Internet. This paper argues that differentiating types of policy is related to ideology and hegemony in the various countries.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".