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Record W6906126167 · doi:10.17605/osf.io/2smr9

Mastering the Narrative: The Role of Conformity and Deviance in Racialized Students’ University Experience

2025· other· en· W6906126167 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeConformityDeviance (statistics)Ethnic groupPersonalityPsychosocialPower (physics)Value (mathematics)

Abstract

fetched live from OpenAlex

Entering university marks a life transition that is significant for the development of one’s identity, personality, and well-being (Azmitia et al., 2013; Chung et al., 2014), and often coincides with emerging adulthood. During this period, young people are engaged in the process of constructing a narrative identity, using their autobiographical past and imagined future to formulate an internalised story of the self and to make sense of their lives. The content and structure of such narratives make up an important component of personality and are important predictors of one’s well-being (McAdams, 2011; McAdams et al., 2010). Racialized students may anticipate unique experiences with social structures (e.g., institutions and social services) that organise and assign value to social categories such as race and ethnicity (Cortina et al., 2012), and that also shape their transition to university (Emerson et al., 2014; Syed & McLean, 2023). The influence of social structures is particularly evident when looking at people’s narratives, as these stories are constructed within the bounds of societal expectations (McAdams, 2010). Specifically, people’s narratives will often draw upon master narratives, culturally shared stories that reflect the power structures of a society (Syed & McLean, 2023). Not adhering to a master narrative can be associated with poor psychosocial development and well-being as one’s story is more likely to be unheard or rejected. Racialized people, due to their unique relationship with social structures, may be more likely to engage with alternative narratives, which function as a form of resistance to push back against the dominant forces of the master narrative (McLean & Syed, 2016; Patterson et al., 2022). Currently, knowledge that could support the success of racialized university students is lacking because personality development research has neglected the specific experiences of racialized people (Arshad & Chung, 2022), especially within a Canadian context (Williams et al., 2022). This study will use a novel, longitudinal mixed-methods approach that combines narrative and questionnaire data to describe whether there is a master narrative of the transition to university and, if so, how it is associated with racialized students’ personal experiences during the transition to university. Namely, we are interested in 1) the content of the master narrative and what that tells us about how people experience social structures within university, 2) who is more likely to conform to normative expectations over time, and who is likely to deviate from and develop alternative narratives over time, and 3) the relation between one’s negotiation with master and alternative narrative and well-being. Participants will be drawn from a recently launched longitudinal study, Giving Voice, examining personality and well-being development in racialized young adults throughout university, with data collection beginning in August 2024 (they began their studies at the University of Toronto Mississauga in September 2024). This research will be the first to examine narratives of the transition to university through a lens of power, privilege, and oppression in a sample of racialized Canadian emerging adults. This will offer unique insights into how racialized people experience important life transitions, whether their experience differs from what is considered normal in society, and any important impacts on personality development and well-being.

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)0.014
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: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.007
Scholarly communication0.0090.005
Open science0.0010.008
Research integrity0.0020.004
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 head0.024
GPT teacher head0.332
Teacher spread0.309 · 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
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
Published2025
Admission routes1
Has abstractyes

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