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Record W4417506549 · doi:10.1080/10720537.2025.2604239

“The Stone Weighs Heavier”: Return, Belonging, and the Dialogical Self

2025· article· en· W4417506549 on OpenAlexaboutno aff
Kaltrina Kusari

Bibliographic record

VenueJournal of Constructivist Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRussian Literature and Bakhtin Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDialogical selfContext (archaeology)Process (computing)Work (physics)

Abstract

fetched live from OpenAlex

This paper offers an autoethnographic account of my doctoral research on the repatriation experiences of return migrant women to Kosova. Grounded in postcolonial theory, transnational feminism, and Dialogical Self Theory (DST), I reflect on the complexities of researching a population to which I am simultaneously connected and distanced—as a Kosovar-born, Canadian-educated woman who chose to return. Through personal narrative and critical reflexivity, I examine how my own migration history, identity, and emotions shaped and were shaped by the research process. Autoethnography served as both a method and mode of theorizing, enabling me to explore tensions between privilege and marginality, insider and outsider, home and away. I use DST to articulate the shifting “I-positions” I inhabited throughout this journey, including moments of discomfort, vulnerability, and relational connection with returnee women. Rather than striving for neutrality, I embrace multiplicity and contradiction as sites of knowledge production. In doing so, this paper challenges simplified researcher position statements and highlights the ethical and epistemological importance of reflexive, relational, and situated inquiry in migration research.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.330
Teacher spread0.324 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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