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Record W1529764102 · doi:10.37119/ojs2011.v17i1.93

"Being With" Bipolar Disorder

2013· article· en· W1529764102 on OpenAlexafffundvenue
J. Karen Reynolds

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsLakehead University
FundersLakehead UniversityUniversity of Regina
KeywordsBipolar disorderPsychologyNegotiationCultural capitalGrounded theoryCertificationSociologyPedagogySocial psychologySocial sciencePolitical scienceQualitative research

Abstract

fetched live from OpenAlex

In this paper, I explore the impact of bipolar disorder on the experiences of two groups of postsecondary students. I theorize that Bourdieu’s (1986) theory of forms of capital provides a lens for understanding how these students negotiate social, cultural, institutional, and symbolic forms of capital in their daily academic lives. I analyze the studies using a constant comparative method often used in research employing ethnographic techniques. The findings examine students’ concerns around learning and achievement within university settings and the debilitating effects of stigma on individuals identified with bipolar disorder. In doing so, the findings reinforce Bourdieu’s theory of capital, because students require relevant support to increase their access to capital in terms of educational certification, employment, finances, and membership in valued groups. However, Bourdieu’s theory has significant limitations. For the bipolar students in these studies, a form of intrapersonal capital, or personal power, was needed to take responsibility for their education and lives, and to positively influence those around them. The implications suggest that instructors in higher education need to accept students with bipolar disorders, while students with bipolar disorders need to reach out to instructors and share their needs.Keywords: bi-polar disorders; post secondary students; higher education

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.287
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2013
Admission routes3
Has abstractyes

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