Role of Consumer Associations in the Governance of E-commerce Consumer Protection
Bibliographic record
Abstract
Despite the proliferation of e-commerce and the growing discussion of how to govern this sector, governance for e-consumer protection is an under-researched area. This study is the first of its kind to employ both quantitative (e-survey) and qualitative (interviews) research to examine e-consumer protection from the view of all three e-governance sectors: e-consumers, e-retailers, and other stakeholders (government, industry, and consumer associations). In particular, this study employed a governance perspective, rather than other perspectives, such as marketing. Focus was aimed toward the tri-sectors' perceptions of six roles of consumer organizations during the e-consumer protection e-governance process. Victoria, Australia was used as a working example. Results suggest that the governance process for e-commerce in Victoria may be compromised due to the lack of overall agreement among the three governance sectors about the role of consumer associations. The tri-sector e-governance model is more appropriate in explaining e-consumer protection than models that eschew the governance process.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".