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Record W3173440532

Role model moms post-secondary academy: A university–community collaboration to encourage access to postsecondary for marginalized women

2023· article· en· W3173440532 on OpenAlexaff
Roxanne Wright, Tianyue Wang, Casey Goldstein, Danielle Thibodeau, Joyce Nyhof‐Young

Bibliographic record

VenueUEA Digital Repository (University of East Anglia) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of TorontoMcMaster UniversityCentre for Family Medicine
Fundersnot available
KeywordsFacilitatorThematic analysisPovertyDescriptive statisticsOutreachPsychologyMedical educationDebriefingResource (disambiguation)SociologyPedagogyQualitative researchComputer scienceSocial psychologyPolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

The Role Model Moms (RMM) program supports low-income mothers completing their General Equivalency Diplomas. Postsecondary education (PSE) can break cycles of intergenerational poverty; however, existing PSE orientation resources were not designed for this group. A need existed for a new university resource utilizing a collaborative and community-engagement approach to provide tailored information on PSE for RMM participants. The RMM Post-Secondary Academy was developed to bridge this gap. It was evaluated via facilitator debriefing sessions, post-event surveys, and participant interviews, with results analyzed using basic statistics and descriptive thematic analysis. The event has run for three iterations, inviting 42, 45, and 38 women, respectively. Participants improved their understanding of and outlook on PSE. Their PSE concerns included family, financial, and academic barriers. This event provides a replicable model for responsive and cost-effective community programming. Community engagement ensured the content was relevant and applicable to the target audience.

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)0.004
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.001
Scholarly communication0.0020.001
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.002

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 head0.032
GPT teacher head0.290
Teacher spread0.258 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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