E-Commerce Diffusion
Bibliographic record
Abstract
As we have already mentioned in the Preface of this book, mentioning EC in this chapter will signify and mean B2C EC unless it is mentioned otherwise. This chapter has primarily addressed, discussed, and conceptualized paradigms of three issues of diffusion of EC. First, we investigated the impacts of EC diffusion on overall social, political, cultural, technological, organizational, and economic relations. In this connection, we revealed seven types of prime relational changes on the effects of EC diffusion in a country context. Consequently, government-private organizations, consumers, and intermediaries (viz., the comprehensive relation of market characteristics) are reshaped. Then we traced the diffusion of EC and the role of different actors associated with this new economical and technological innovation, viz. EC, and their functional characteristics in the diffusion process. Based on the association of those stakeholders with the market economy, and the role and functions of those stakeholders in the diffusion process of EC, we conceptualized the theoretical framework for diffusion of EC. From this framework, we revealed that the government role, capability, and globalization policy; consumer preferences; private organization’s capabilities; the global E-organizations mission; and the infrastructure and market mechanism factors with a set of associated variables create the ability for EC to be diffused in any country. Therefore, the diffusion of EC is not a unidimensional issue, but has multi-dimensional aspects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".