Valuing data: Where are we, and where do we go next?
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 importance of data as a driver of technological advancement cannot be underestimated, but how can it be measured? This paper looks at measuring the value of data in national accounts using three different categories of data-related assets: data itself, databases and data science. The focus then turns to three recent studies by statistical agencies in Canada, the Netherlands and the United States to examine how each country uses a cost-based analysis to value data-related assets. Although there are two other superior ways of valuing data (the income-based method and the market-based method, as well as a hybrid approach), the authors find that these methods will be difficult to implement. The paper concludes with recommendations that include widening data-valuation efforts to the public sector, which is a major holder of data. The social value of data also needs to be calculated by considering both the positive and negative aspects of data-related investment and use. Appropriate data governance strategies are needed to ensure that data is being used for everyone's benefit.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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 it