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Record W2103249137 · doi:10.26522/brocked.v22i2.344

Negotiating the Confluence: Middle-Eastern, Immigrant, Sexual-Minority Men and Concerns for Learning and Identity

2013· article· en· W2103249137 on OpenAlexaffvenueabout
Matthew Eichler, Robert C. Mizzi

Bibliographic record

VenueBrock Education Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsImmigrationCitizenshipTransformative learningGender studiesSexual identityIdentity (music)SociologyNegotiationQualitative researchPsychologySocial psychologyPolitical sciencePedagogyHuman sexualityPoliticsSocial scienceLaw

Abstract

fetched live from OpenAlex

Sexual-minority male immigrants re-locating from the Middle East to the United States and Canada have particular experiences upon entry and integration into their new societies. The needs of learning and identity are highlighted through a multiple case approach involving three men. Interviews were conducted with the three participants, which were analyzed by the authors using qualitative case analysis. The data highlights the unmet expectations for life as a new immigrant, as well as the complexities of becoming involved in sexual-minority settings. Their learning experiences may be explained using a theoretical framework of transformative learning. These findings suggest that sexual-minority immigrants have complex needs, such as identifying with appropriate communities and deconstructing false representations of “gay rights” and citizenship in popular culture. Educational and social programs could address these needs when considering what might be important for immigrant adult learners.

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)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0260.021
Scholarly communication0.0100.006
Open science0.0010.010
Research integrity0.0030.004
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 head0.027
GPT teacher head0.344
Teacher spread0.317 · 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

Citations5
Published2013
Admission routes3
Has abstractyes

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