Estimating International Migration on the Base of Small Area Techniques
Bibliographic record
Abstract
Population migration flow is a component of population facing difficulties in measuring in the inter-census period of time. The rationale of this study is that Romanian statistics on international migration flows are of very poor quality, the availability of data on past trends being strongly limited, provided only from administrative sources. For this reason, in the inter-census period, the variable of interest is provided by the labour force survey available at national and regional level every quarter of the year since 2004. The smaller disaggregation like localities level using direct estimators conducts to results of unreliable estimates and will surely lead to higher standard error and consequently, high coefficients of variation. The main reason for this is the insufficient number of respondents or no respondent at all in a small domain. Small area estimation techniques are able to carry out the estimation at the localities level (NUTS 5). The main purpose is to provide methods able to estimate the population in Romania, based on the Labour Force Survey and also the results of 2002, respectively 2011 population census.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".