MétaCan
Menu
Back to cohort
Record W4362241857 · doi:10.1353/gpr.2022.0019

Finding Refuge in Canada: Narratives of Dislocation ed. by George Melnyk and Christina Parker

2022· article· en· W4362241857 on OpenAlexaboutno aff
Gemechu Abeshu

Bibliographic record

VenueGreat Plains research · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeNarrativeGeorge (robot)MemoirMedia studiesSociologyHistoryArt historyArtLiteratureArchaeology

Abstract

fetched live from OpenAlex

Reviewed by: Finding Refuge in Canada: Narratives of Dislocation ed. by George Melnyk and Christina Parker Gemechu Abeshu Finding Refuge in Canada: Narratives of Dislocation. Edited by George Melnyk and Christina Parker. Edmonton: Athabasca University Press, 2021. 196 pp. $27.99 paper. Despite my professional fascination with the topic, I began reading Finding Refuge in Canada: Narratives of Dislocation with trepidation. It has been my experience that many manuscripts on this subject are either lengthy policy critiques or amateurish memoirs. Extraordinarily, this 187-page-long compendium organized into 14 chapters is enchantingly readable and Hitchcockian. I began reading this book from “Once a Refugee, Always a Refugee,” a captivating chapter by George Melnyk who came to Canada as a refugee in 1949 and now is a professor emeritus of communication, media, and film at the University of Calgary. His chapter tells such a compelling story I urge anyone to read it. This book, a compilation of first-person accounts by 17 contributors from diverse backgrounds—including the co-editors George Melnyk and Christina Parker— narrates the refugee experience in Canada. It includes perspectives from frontline workers, academics, advocates, refugee sponsors, and civil servants. Having gone through the book cover to cover, I appreciate that it appropriately acknowledges that millions of people are displaced each year by war, persecution, and famine, and the global refugee population is persistently on the rise. It also reminds the readers that although Canada is a benevolent country that accepts refugees from all over the world, refugees have encountered different types of reception upon arrival. Through the individual narratives, it becomes evident that Canada’s refugee system is inconsistent, disjointed, and struggling to manage the current backlog and continued influx of refugees. These narratives challenge mainstream public discourse about refugee identities and histories and provide profound insight into the social, political, and cultural challenges [End Page 181] and opportunities refugees experience in Canada. Through their accounts, the individual stories dress humanity onto the global refugee crisis and challenge readers to reflect on the transformative potential of more equitable policies and processes. These individual narratives emphasize the human toll that refugees endure, covering topics about arriving in Canada, how Canada responds to refugees, and the struggle for inclusion. This book, therefore, is a timely publication that calls to action those in positions of power and privilege to understand the nuances and intricacies involved in a process that has life-and-death consequences. Finding Refuge in Canada is informative, compelling, and apt for all readers: academics, advocates, policymakers, and service providers alike. I would recommend it to settlement workers, refugee sponsors, advocacy groups, students, and those interested in learning more about Canada’s refugee determination system and Canada’s varied responses to refugees throughout recent history. Gemechu Abeshu Centre for Refugee Studies York University Copyright © 2023 Center for Great Plains Studies

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.011
Science and technology studies0.0470.020
Scholarly communication0.0180.007
Open science0.0030.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0120.002

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 head0.054
GPT teacher head0.379
Teacher spread0.325 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueGreat Plains researchSame topicMigration, Health and TraumaFrench-language works237,207