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Record W4405638837 · doi:10.1080/13603124.2024.2442624

As sunflowers face the sun: exploring the experiences of Black and Indigenous women educational leaders

2024· article· en· W4405638837 on OpenAlexafffund
Whitneé Garrett-Walker, Jennifer Brant, Jasmine Pham, Nia Spooner

Bibliographic record

VenueInternational Journal of Leadership in Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Toronto
FundersConnaught Fund
KeywordsIndigenousGender studiesSociologySolidarityIntersectionalityStorytellingEducational leadershipQualitative researchCritical race theoryIdentity (music)Public relationsPedagogyRacismPolitical sciencePoliticsSocial science

Abstract

fetched live from OpenAlex

Currently, the field of educational leadership continues to exclude the crucial voices of historically marginalized school administrators. As such, this study sought to learn more about the experiences of Black and Indigenous women educational leaders. To contribute to the scant literature available the authors ethically and intentionally recruited participants who self-identified as women, Black, Indigenous or Afro-Indigenous. The data collection for this qualitative study included individual interviews with 15 participants that were then analyzed and coded on NVivo in accordance with Black feminist thought, Indigenous feminisms critical race theory in education, specifically, the tenets of intersectionality and counter-storytelling. Findings from the study suggest that Black and Indigenous women school administrators have much in common in their approach to leadership. For instance, the collective ways in which they engage in the world, and how they define healing and their collective need for it. Finally, this study serves as an opening space for Black and Indigenous women school leaders not only share their lived experiences, but to address cross-identity solidarity that is specific to Black and Indigenous peoples.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.408
Teacher spread0.291 · 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 teacher head, 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

Citations1
Published2024
Admission routes2
Has abstractyes

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