MétaCan
Menu
Back to cohort
Record W4392003205 · doi:10.1177/15413446241234236

Identity, Belonging and Agency: A Transformative Development Framework for Global Africans/Black Peoples

2024· article· en· W4392003205 on OpenAlexaff
Yabome Gilpin‐Jackson

Bibliographic record

VenueJournal of Transformative Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTransformative learningAgency (philosophy)Gender studiesNarrativeSociologyIdentity (music)Context (archaeology)Identity formationAestheticsSelf-conceptSocial scienceHistoryPedagogy

Abstract

fetched live from OpenAlex

1. Who am I? 2. Where do I belong? 3. What am I called to? These three questions represent the narrative shifts that are the outcomes of the Identity/Belonging/Agency (IBA) transformative development framework. The IBA framework emerged from the author’s critical reflections on fiction reading and dialogues in 12+ community conversations to explore everyday global African/Black experiences. It responds to the self-inquiry: How do global Africans/Black peoples experience developmental transformation in the context of social marginality? It conceptualizes that the key developmental tasks of global Africans/Black peoples lies in claiming identity through differentiation from dominant narratives of marginality, belonging through locating self-in-society and community, and agency through a focus on self-in-transcendence. The IBA framework is proposed as core to understanding how global Africans/Black peoples, and perhaps other socially constructed racialized groups, can choose to move from marginality to personal as well as social transformation through their agency.

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)0.014
metaresearch head score (Gemma)0.004
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0170.066
Scholarly communication0.0140.012
Open science0.0020.015
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.379
Teacher spread0.354 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Transformative EducationSame topicYouth Education and Societal DynamicsFrench-language works237,207