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Record W4387312075 · doi:10.1007/978-3-031-41348-3_20

Immigrant Stories

2023· book-chapter· en· W4387312075 on OpenAlexaboutno aff
Thabata da Costa

Bibliographic record

VenueIMISCOE research series · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFace (sociological concept)RefugeePerceptionPsychological resilienceSociologySocial psychologyMedia studiesPolitical sciencePsychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract The migrant experience is above all a humbling one. There is often a wrong perception amongst those who live in places where migrants tend to go that the resilience we carry is due to some negative experiences we had where we came from; and that this justifies the hardships we endure in the places we choose to call home. The story is frequently very different. We move full of hopes and dreams, and many would not take the leap if conscious of all the barriers we are sure to face. The stories of migration are as diverse as the number of stars in the sky. And for that reason, the idea of migrants as a single group goes against any kind of reality if examined for more than a second. Although in Canada there are established categories of immigrants, such as economic migrants and refugees (and several others), within these categories are human journeys that are specific to each individual. Even inside a family we find different reasons for taking the leap. While one can choose to move away to have better professional opportunities, others might only be looking to get away from the mess their families are tangled in. To distance oneself from one’s birthplace can encompass the understanding that one’s comfort zone lies in a distant place, far away from social pressures – a chance to reinvent yourself as an individual.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.136
GPT teacher head0.425
Teacher spread0.290 · 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 designTheoretical or conceptual
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 routes1
Has abstractyes

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