Environmental infomediaries in the Risk Society: The behavioral impact of online environmental information and communication strategies
Bibliographic record
Abstract
The expansion of the online marketplace changed the way many people consume products and information by allowing consumers to conduct increasing amounts of pre-purchase research. Many organizations developed online tools to help consumers efficiently find and use environmental information to make more sustainable purchase decisions. In this paper I explore the impact and efficacy of these online environmental infomediaries through an analysis of their history, methods, and impact. Using a framework developed from Ulrich Beck’s theory of the Risk Society and Bettman’s theory of contingent decision making, I conducted a preliminary case study of GoodGuide.com, a well-known online environmental infomediary. Based on this framework, I found that effective online environmental infomediaries (1) target educated, internet-savvy, leisure- and trend-oriented consumers; (2) focus on high-risk, non-convenience purchases; (3) provide visually appealing and interactive tools; (4) ensure information tools are easy to use and understand; (5) employ a clear and transparent methodology; and (6) satisfy consumers’ expectations of their efficacy. GoodGuide.com excelled at several of these criteria, but its opaque methodology and failure to meet most consumers’ expectations may threaten its long-term viability.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".