Bibliographic record
Abstract
Data are invaluable. How can we assess the value of data objectively and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, data management, data mining, electronic commerce, and marketing. In this tutorial, we present a unified and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing, review the development and evolution of pricing models, and compare the proposals of marketplaces of data. We cover both digital products, such as ebooks and MP3 music, and data products, such as data sets, data queries and machine learning models. We also connect data pricing with the highly related areas, such as cloud service pricing, privacy pricing, and decentralized privacy preserving infrastructure like blockchains.
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.
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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.013 | 0.039 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".