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Record W4389711767 · doi:10.1515/9780776638072-002

Foreword

2023· book-chapter· en· W4389711767 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity of Ottawa Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration, Health, Geopolitics, Historical Geography
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEuropean Commission
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

15 different countries, collected data on borders since 2012.Our core research focus was to challenge the well-established conception that borders are primarily understood as sovereign territorial boundaries that emerge out of international treaties.The research program thus addressed fundamental how, why, and what questions about borders, a very important contribution to knowledge in a globalizing world when movement is increasingly scrutinized everywhere, and not just at the sovereign boundary line of states, and at a time, when goods seem to travel more easily than humans.Indeed, the regulation of human flows across borders is fraught and highly contentious, and even today humans die crossing borders in the thousands yearly.Our team studied border history and culture, mobility and security, environmental sustainability and governance.As illustrated by this book, and a number of other books published with BIG_Books and the University of Ottawa Press series on Borders in Globalization, our research program initially approached those questions from the perspective of territories, regions, and states, to collect evidence that there were multiple challenges to the "territorial trap" assumption.In Borders and Migration: The Canadian Experience in Comparative Perspective Michael Carpenter, Melissa Kelly, and Oliver Schmidtke have edited a volume comprising 12 chapters, an introduction, and a conclusion, which carefully review, document, and analyze how Canadian borders and immigration policies are increasingly intertwined.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.248
Teacher spread0.210 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes2
Has abstractyes

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