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Record W4410496559 · doi:10.15173/ijsap.v9i1.5989

Harmonizing identities

2025· article· en· W4410496559 on OpenAlexvenueno aff
Nira Rahman

Bibliographic record

VenueInternational Journal for Students as Partners · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

This reflective essay examines how my intersecting identities as an educator and musician shape my approach to teaching and staff-student partnerships. Inspired by Cook-Sather's (2015) concept of identity, I explore how navigating multiple cultural identities, educational systems, and musical traditions has profoundly influenced my teaching philosophy. My classical music training, rooted in an authoritarian approach, contrasts with my Western educational journey, which encouraged independence. These distinct experiences have cultivated an appreciation for diverse pedagogical styles, each uniquely suited to their cultural contexts. My musical training also enhances my sensitivity to the emotional and expressive dimensions essential in building equitable partnerships. This essay reflects on how my cultural lens, shaped by my musical background, informs my understanding of partnership values and how students interpret these values based on their lived experiences. This reflection highlights how my dual identity enables me to foster inclusive, dynamic spaces that embrace diverse perspectives within 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 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)0.009
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0160.037
Scholarly communication0.0180.017
Open science0.0020.027
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.003

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 head0.126
GPT teacher head0.495
Teacher spread0.370 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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