MétaCan
Menu
Back to cohort
Record W2586793975

2007 world population data sheet.

2007· article· en· W2586793975 on OpenAlexaboutno aff
Kearney Ms, Levine Pb, Jones Le, Michèle Tertilt, Claro Rm, Carmo Hc, Machado Fm, Carlos Augusto Monteiro, Assunção Mc, Santos Ida S, Barros Aj, DP Gigante, César G. Victora, Terry A. Klein, Jan Eckhard, J. Paul Robinson, Janine Young, Subhash Pokhrel, Kay T, D Choi, M Chai, Marco Roncarati, Sunita Tata, Chen Xs, Yin YP, Chen Lp, Y-H Yu, Wei Wh

Bibliographic record

VenueSocial Science & Medicine · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeImmigrationDeveloping countryDeveloped countryGeographyDevelopment economicsPopulationPolitical scienceEconomic growthDemographySociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Much press has been given to the increase in immigration in the industrialized world-most of which has come from developing countries. The United States and Canada for example both have long traditions of immigration while many countries in western Europe have seen the influx of migrants from both former colonies in Asia and Africa (to the Netherlands) and eastern Europe (to Ireland). Less well known however is that several countries in the developing world have seen a rise in their foreign-born populations. Costa Rica for example has long attracted refugees escaping civil strife in nearby countries and more recently has been a destination for Nicaraguans and Panamanians seeking seasonal work. Botswana provides another case in point as it has attracted both refugees and economic migrants from its neighbors in southern Africa. (excerpt)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.103
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.008
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1030.109

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 head0.082
GPT teacher head0.346
Teacher spread0.264 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations1,420
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueSocial Science & MedicineSame topicHIV/AIDS Impact and ResponsesFrench-language works237,207