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Record W4224954386 · doi:10.5281/zenodo.6497223

How key actors and stakeholders apply the notion of vulnerability in Europe, Canada, and South Africa

2022· report· en· W4224954386 on OpenAlexaboutno aff
Christine Jacobsen, Marry-Anne Karlsen, Jo Vearey

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typereport
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsKey (lock)Vulnerability (computing)Political scienceGeographyRegional scienceEnvironmental resource managementComputer scienceComputer securityEconomics

Abstract

fetched live from OpenAlex

This introduction serves to contextualize six PROTECT country case study reports from the EU Horizon 2020 PROTECT research project. Drawing on ethnographic studies in selected arrival ports in France, Italy, Spain, Greece, Canada and South Africa, the reports discuss how actors and stakeholders in the field level governance of migration and international protection understand and operationalise the notion of vulnerability. In this introduction, we first discuss how the notion of vulnerability is understood in the Global Compacts for Migration and on Refugees. We go on to present some analytical approaches to vulnerability to frame the reports, and end by drawing out some cross-cutting themes from the case studies. In particular, we highlight certain structural challenges and adverse effects related to the increasing centrality of vulnerability in migration governance.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.086
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0240.016
Scholarly communication0.0180.005
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.098
GPT teacher head0.265
Teacher spread0.167 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations3
Published2022
Admission routes1
Has abstractyes

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