Business Data Collection: Toward Electronic Data Interchange. Experiences in Portugal, Canada, Sweden, and the Netherlands with EDI
Bibliographic record
Abstract
Abstract This article discusses the experience and the ideas of National Statistical Institutes from four countries – Portugal, Sweden, Canada, and the Netherlands – in order to build a fully automated data collection system, to provide a system-to-system (S2S) data exchange or Electronic Data Interchange (EDI) between all stakeholders in the production chain. This joint work is a summary of an invited session at the Fifth International Conference on Establishment Surveys, which was devoted to ‘the future of business data collection’. Taken together, the four presentations provide an overview of recent experiences with S2S/EDI data collection for financial business data. The basis for such a system is an integrated unbroken digital information chain that runs from the recording of financial data in computerised administrative systems of individual businesses all the way to publishing economic statistics – the Business Information Chain. This chain can be ‘closed’ and made into a cycle by including a feedback loop, for example by providing benchmark data to businesses. However, to make it happen, technical standardisation, vertical and horizontal conceptual harmonisation between all partners in the chain, and positive business cases for all partners are needed. The article starts by putting EDI developments in historical perspective.
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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 | 0.088 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".