Linking Trade Data from Different National Statistical Offices Through a Private Set Intersection
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
National Statistical Offices (NSOs) collect extensive data on the international activities of firms within their borders. However, they typically lack information about the foreign partners with whom these firms trade. Linking import data from one NSO to corresponding export data from a partner NSO could significantly enhance statistics on firms’ international operations. While technically feasible, such linkage is legally constrained by strict privacy laws. Private set intersection (PSI) protocols may help address privacy concerns but require unique identifiers to avoid linkage errors. To overcome this limitation, we propose a PSI protocol with three innovations. First, we estimate the rates of linkage error by modeling the number of links from a given record. Second, we adjust an estimated population mean according to the estimated linkage accuracy. Lastly, our adjustment explicitly accounts for this accuracy without assuming a particular relationship among the target variables.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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