Positioning Online Consumer Reviews (OCRs) as a Form of Regulatory Governance and Exploring Methods for Addressing OCR Limitations
Bibliographic record
Abstract
This thesis investigates the online consumer review (OCR) mechanism and process by positioning OCRs within the existing state and non-state regulatory structure, identifying limitations and problems of OCRs from multiple perspectives, and suggesting possible ways of addressing these limitations and problems. It examines the OCR mechanism to understand where it fits as a regulatory tool within the existing government and non-state set of regulatory arrangements, using the sustainable governance (Webb, 2005) concept and framework as a lens for analysis. The thesis suggests that OCRs are a new non-state way of regulating business behavior in which an online platform is created by a firm, and this platform provides a structured process for individual consumers to make and publish reviews of individual businesses, who then respond to these reviews in an effort to maintain or increase their profitability. The thesis then identifies key problems with the OCR approach and explores how conventional state-based approaches to consumer information (e.g. laws) and non-state approaches (e.g., multi-stakeholder standards) can address these problems, and by so doing, move from the current ad hoc state/non-state approach for the dissemination of consumer information about businesses to a more systematic and coordinated approach, in keeping with the concept of sustainable governance. The thesis draws on a literature review as well as surveys and semi-structured interviews to support its analysis.
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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 | 0.242 | 0.391 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".