Environmental infomediaries in the Risk Society: The behavioral impact of online environmental information and communication strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it