The e-governance approach to register-based census, based on the case of the GCC countries: A research note
Bibliographic record
Abstract
This paper discusses the experience of the Scandinavian countries with respect to register-based census (RBC), outlining important enabling features that facilitated this type of accounting system in Scandinavia. The central question examined is whether RBC is possible in the Gulf Cooperation Countries (GCC) as they proceed toward the 2020 round of census. The secondary question is whether, for the GCC countries, the e-governance approach offers a viable alternative to the classical Scandinavian approach to RBC.L’article traite de l’expérience de pays scandinaves en matière de recensement à registre (RR); il donne les importantes caractéristiques qui permettent de faciliter ce genre de système de comptabilité en Scandinavie. La question centrale qui y est examinée consiste à déterminer si le RR est possible dans les pays du Conseil de coopération du Golfe (CCG) en vue du prochain recensement de 2020. L’autre question consiste notamment à savoir si, pour les pays du CCG, l’approche d’une gouvernance électronique fournit une solution de rechange viable à l’approche scandinave classique au RR.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".