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Record W2981847326 · doi:10.1177/0034523719882455

How does a sense of belonging develop in postsecondary? A conceptual Belonging in Academia Model (BAM) from sighted perspectives

2019· article· en· W2981847326 on OpenAlexaff
Minnie Teng, Mary-Lou Brown, Tal Jarus, Laura Yvonne Bulk

Bibliographic record

VenueResearch in Education · 2019
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConstruct (python library)Focus groupPerceptionGrounded theoryPsychologySociologySocial psychologyPedagogyQualitative researchSocial scienceComputer science

Abstract

fetched live from OpenAlex

Belonging is associated with increased engagement in academic pursuits and well-being. However, there is a lack of research on how a sense of belonging develops in academia. The academic environment comprises largely of sighted individuals. Exploring sighted students, staff, and educators’ perceptions of belonging contributes to our understanding of environments that foster belonging for sighted individuals and their perceptions of the same for people who are blind. We conducted focus groups and in-depth interviews with 25 sighted people in academia to investigate how sense of belonging develops for them, and what facilitators and barriers would impact belonging in academia. Grounded theory was used to construct the Belonging in Academia Model (BAM), which explores how people perceive, create, and develop belonging in academia.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.015
Scholarly communication0.0070.010
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.429
Teacher spread0.368 · 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 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

Citations17
Published2019
Admission routes1
Has abstractyes

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