Information strategies to support full information product pricing: The role of trust
Bibliographic record
Abstract
In this paper we report on the importance of trust in the development and operation of distribution networks that attach non-price information to products to mitigate market dynamics introduced by information asymmetries. Often this non-price information is transmitted from producers to consumers through trusting networks or under certifiable labels such as "organic" or "Fair Trade." We are calling such networks Full Information Product Pricing (FIPP) Networks. This study is part of a larger project aimed at understanding how a suite of future-possible data interoperability standards and social computing technologies will set the stage for a set of product labelings, information architectures and policies that may have the potential to supplement a compliance-enforcement approach with a more market-based voluntary approach to significantly expand the share of worker- and environmentally-friendly products within the NAFTA region. This initial exploration of four cases in Canada and Latin America indicated that trust, in the forms of institutional trust, calculative trust, and relational trust, plays key roles in FIPP operations and expansion. It is critical for building collaboration, coordinating network activities, and mitigating the risks associated with information asymmetry.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".