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Record W2908197829 · doi:10.4236/ojl.2018.74016

Innovations in Leadership Development: Centering Communities of Color

2018· article· en· W2908197829 on OpenAlexaff
Ann Curry‐Stevens

Bibliographic record

VenueOpen Journal of Leadership · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPeople of colorPublic relationsEquity (law)Leadership developmentCurriculumGraduation (instrument)Foundation (evidence)Community developmentPolitical scienceWork (physics)SociologyPedagogyEngineering

Abstract

fetched live from OpenAlex

With innovative funding from a large local foundation, communities of color in Portland, Oregon developed an array of leadership programs to serve communities of color. This article shares the models they developed, including overviews of curriculum, theories of change, and concrete evidence-based gains achieved by the programs. Innovations include a leadership model that is rooted in community leadership, and the emergence of community priorities to guide the programs, alongside culturally-specific programs that are effective in reaching and supporting the participation of emerging and existing leaders of color. Community priorities included advocacy engagement that resulted in achieving real gains during the yearlong program, and preparing leaders to engage in racial equity work in public and institutional policy after graduation. Highlighted are the distinct assets of culturally specific programs that were perceived to be responsible for achieving significant gains. Conclusions emphasize the importance of culturally specific leadership programs for reaching and centering leaders of color and the ways that such investments hold potential to lead equity efforts in the community and in organizations. Avenues for strengthening programs and their evaluation conclude the article.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.057
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.483
GPT teacher head0.398
Teacher spread0.085 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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