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Record W4403430032 · doi:10.15173/ijsap.v8i2.5590

Reflecting on vulnerability, skill-building, and identity in an interdisciplinary SaP project

2024· article· en· W4403430032 on OpenAlexaffvenue
Matthew Dunleavy, Susan Andrews, Carla VanBeselaere, Christelinda Laureijs, Shannon Goguen, Denise Roy-Loar

Bibliographic record

VenueInternational Journal for Students as Partners · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsYork UniversityMount Allison University
Fundersnot available
KeywordsVulnerability (computing)Identity (music)Engineering ethicsSociologyEnvironmental planningPsychologyEngineeringGeographyComputer scienceAestheticsComputer securityArt

Abstract

fetched live from OpenAlex

Launched in 2021 by a team of undergraduate students, university faculty, associate researchers, and community partners collaborating as genuine equals in a diverse team, the Together Time Story Sacks intergenerational literacies program forms part of an ongoing action research project aimed at understanding and addressing barriers that residents of rural regions face in accessing literacies programming. In this paper, six team members who co-imagined, co-designed and co-implemented Together Time but occupy different roles on and beyond university campuses reflect on the ways the students-as-partners (SaP) model, through which we brought Together Time to life, shaped both our process and our outputs during the project’s pilot phase (September 2021–August 2022). We suggest that empowering humans with diverse academic and lived experiences through the SaP model is an Invigorating, messy, and, at times, nerve-racking process but an eminently fruitful enterprise that, in our opinion, produces rich research with and for our community while transforming our understanding of education and ourselves.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.015
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0260.032
Scholarly communication0.0100.006
Open science0.0020.030
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.120
GPT teacher head0.714
Teacher spread0.594 · 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

Labeled directly by 3 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainMethods
GenreEmpirical · Commentary

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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

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