Bibliographic record
Abstract
It was as early as 1960s when some countries such as the United States and United Kingdom have released anonymized resampled micro data created from censuses and surveys followed by Canada and some Nordic countries. Many analytical findings with micro based data have carved out new frontiers not only in academic research but also in their policy use that traditional table-based analyses could never promise. In addition to the enormous contributions in offering academic findings, micro data have created new dimensions in terms of potential usability of statistical data. New types of datasets such as longitudinal and panel data created from individual records have cultivated a new arena of dynamic data analysis that neither cross sectional nor time series data could afford. Japan is far behind other foreign countries in terms of the use of micro data. Government has been hesitant in creating public use micro data files in Japan due partly to the apprehensions for the potential threat of privacy issues and partly to the conventional practice of data enclosure within institutions. Governments' negative responses to the disclosure of micro data have also been supported by the strict application of confidentiality clause stipulated in Japanese Statistics Law. During a couple of decades after 1980, however, many countries which appeared to be new member countries with public use micro data files have amended their legislative frameworks one afteranother so as to adjust confidentiality clause with launching systems to provide public use micr data files. This paper aims to bring to the light the impending challenge of Japanese statistical system.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".