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Record W4245652484 · doi:10.1111/imig.12203

Editorial

2015· editorial· es· W4245652484 on OpenAlexaff
Howard Duncan

Bibliographic record

VenueInternational Migration · 2015
Typeeditorial
Languagees
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCarleton University
Fundersnot available
KeywordsRefugeeState (computer science)ImmigrationHappeningPolitical scienceData collectionPublic relationsLaw and economicsSociologyLawHistoryComputer scienceSocial science

Abstract

fetched live from OpenAlex

Data are the lifeblood of the social sciences, and it is our constant quest to acquire new data and to develop new ways to urge more and more knowledge from them. Migration data are particularly elusive given the lack of consensus regarding what to count or who ought to be considered a migrant, the fact that many migrants do not want to be counted and take precautions to prevent this from happening, and given that few governments record departures. One of the authors in this issue, Jonathan Moses, refers to the “appalling state of migration data” in the world today and notes the costs that this has for migration policy in societies both of origin and destination. Many countries that willingly accept if not actively recruit immigrants have good data on who has arrived and under what visa, but at the same time do not know who has left, let alone for what duration. Migration data are difficult and costly to obtain, and net migration data additionally so. This makes it all the more challenging for governments to manage migration even at the best of times. And these are not among the best of times for migration management. In this issue, we offer five articles on data collection and analysis, articles that confront the “appalling state of migration data” and lead the way to a better state of affairs through innovations in collection, estimations, analysis, and practical applications. We open with a remarkable innovation in ascertaining refugee populations, one that was borrowed from a way of estimating wildlife populations that was subsequently adapted for epidemiology and later by demographers and even by astronomers for estimating numbers of celestial bodies. The capture-recapture approach to estimating populations is applied to refugees in smaller geographical locales by Gold et al, and they demonstrate how this method can be used by local authorities in allocating resources for services to refugees. Makaryan, noting the many alternative methods for estimating migrant populations, considers the special problems in so doing in developing countries, specifically 15 states of the former Soviet Union. She notes the variety of definition of ‘migrant’ used and the attendant ambiguities that this lends the data, the failure of censuses to capture temporary migrants, and the provisional value of household surveys in measuring migration. Moses has responded to the state of migration data by launching EMIG 1.2: A global time series of annual emigration flows, an open-source database that is in its early stages of development but yet already offers significant potential to enhance our understanding of emigration should the global migration community participate in further developing the database. Analyses of the data to date confirm that not only are migration rates lower now than they were early in the twentieth century, they have been falling since 1994, something that will take many of us by surprise. Bailey and Lau turn our attention to Hong Kong which has undergone a major shift in migration since the re-unification with China which has led to a highly dynamic two-way flow of workers, students, and settlers. They propose a new method for categorizing and measuring flows as well as new institutional mechanisms to co-ordinate data collection with policy making. In an article that bridges our two themes for this issue, Mendoza examines emigration patterns from a municipality in Mexico City, seeking insights by comparing households with and without emigrants. Her logistic regression models reveal the signal importance of social networks in motivating departures. Our second theme for this issue is the motivations for emigration. Seven articles contribute important nuances to our understanding of what underlies people's decisions to leave their homelands, nuances that we hope will further enliven both the theoretical debates about what lies behind decisions to migrate and enhance the level of understanding among policy organizations. In looking at contemporary Kosovo, a fledgling country suffering from very high levels of departures, Ivlevs and King note the lack of confidence in the future of the country and its economy amongst especially those Albanian Kosovars with higher levels of education. Emigration aspirations have returned to levels not seen since before independence, a trend that may itself fuel an even greater demand to leave this struggling country. Cohen, Duberley, and Ravishankar consider how Indian scientists employ international mobility as a career enhancer, a strategy that allows them gain valuable international experience while preserving their cultural ties to India as well as their ability to return to India in a more advanced position. Weeks and Weeks examine the role of transnationalism in contemporary emigration from Latin America to the United States, going beyond the lure of better-paying jobs that support remittances to the supportive roles increasingly played by their homeland governments in protecting their rights while abroad and encouraging their return. Staying within Latin America, Silva and Massey detail the role of violence in motivating migration out of Columbia. Not as straightforward as one might be tempted to imagine, violence tends to lead to emigration predominantly for those with higher levels of education and stronger social networks abroad. While violence can bring about a decision to leave, it is social capital networks that determine destinations. Bylander brings us to Cambodia to explore how actual and anticipated environmental distress motivates decisions to emigrate. Gerver brings the perspective of moral philosophy to voluntary repatriation, offering a careful normative analysis of the tension between facilitating repatriation to restore rights and ensuring that the repatriation is in fact voluntary. Finally, we return to a post-Soviet state to look at mass emigration from Lithuania, the first Soviet state to declare independence in 1990. Klusener et al use census and registration data to document that it is characteristics such as employment status, education, and prior migration experience that influence decisions to leave.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.007

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.

Opus teacher head0.019
GPT teacher head0.359
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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