Adaptation of Statistics Canada and Eurostat methodologies for variance estimation of changes of the main labour force indicators in Iran
Bibliographic record
Abstract
The changing values of the indicators obtained from national labour force surveys provide analysts and planners with valuable information on the fluctuations of the labour market of the country. Labour force surveys in many countries follow the standards established by the International Labour Organization, and, as a result, tend to be similar in various respects. Given these similarities, the procedures used by the statistical organizations of Canada and the European Union are examined in this paper for the development of variance estimates of changes of the labour force indicators in Iran. While the survey in Iran and those in the countries under study have many similarities, they also differ in certain respects, namely, in terms of the periodicity of the survey, the rotation pattern as well as the unit of rotation, and the possible existence of non-response among the primary sampling units. Here, first, the methodologies of Statistics Canada and Eurostat are modified and adapted to the particularities of the labour force survey in Iran. Then, the results are compared. Among the four methods examined, the bootstrap methodology of Statistics Canada, after some modifications and adaptations, is found to be especially suitable for application in the labour force survey of Iran and, perhaps, in other counties with similar conditions. The proposed methodology can, particularly well, take into account the impact of the various steps of weight calculations on the variance estimates of change of the main labour force indicators.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".