Concept and Experiment of an International Demographical Information System
Bibliographic record
Abstract
In recent years,international migration has become more and more common,while its patterns are getting increasingly complex.After several years living in a certain destination,immigrants often make repeat migrations,either to return to their home country or onward to another host country.Researchers have developed new methodologies to study the flows and patterns of transnational movements,and new theories to explain them.One of the new developments in this respect is the triangular model of human capital transfer among nations by Devoretz and Ma(2002),which emphasizes the dynamic nature of international migration in the context of regional development.It argues that immigrants often enter into an entrepot country to accumulate transnational human capital and other capital,such as citizenship.After acquiring such capital,they can choose to stay in the country,return to their home countries,or move to another host country,in order to maximize the return to their acquired transnational capital.New challenges are posed to the study of transnational flows of human capital,requiring the use of multiple censuses of the sending country,entrepot country and major hosting country.Due to structural differences,censuses from different countries are normally in different format and cover populations within different national boundaries.As a result,the most current research on international migration is often limited to either the sending or receiving countries.Efforts are needed to integrate these data sets into a standardized one,so that variables can be unified for direct comparison and further analysis.We adopt a new approach to the integration of census micro-data of different countries into one unified framework.The framework includes two major parts: the statistical part and the mapping part.We start with the statistical part by using open source software packages,such as PHP and MySQL,to implement an integrated micro database system.The micro database includes censuses from multiple countries/regions,including the US,Canada and Hong Kong.In order to enable automated analysis,we first select common variables from different censuses and then standardize each of them to the same unit or category.These standardized variables are either called identifiers or indicators.Identifiers are variables used to identify similar population groups from different censuses and indicators are variables used to compare among groups.In the demo system,we used a total of 10 identifiers and 3 indicators.With the integrated database,we designed a search module and a statistics module.The search module uses key identifiers to search specific population groups from different censuses.The result is listed as tabulations to support further studies.The statistics module takes previous tabulations as input and output results of statistical analysis,including cross country/region comparison and uni-variable analysis(maximum/minimum/mean/std) as tables,graphs,and pre-map files.The second part of the framework is to integrate the micro database with a GIS database,the result of which can be used for mapping purposes.The statistical outputs from Part 1 are processed by the mapping module of Part2,and the system creates online map visualization.This paper mainly introduces the first part of the framework.We are still working on the second part.When both parts are finished and integrated,this system will integrate spatial information with census micro-data of different countries/regions,and provide a unified web-based demographic information system to facilitate flexible and advanced international immigration analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".