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Record W143540889 · doi:10.5206/cie-eci.v43i1.9239

Educational Leadership in a Fragile State: Comparative Insights from Haiti

2014· article· en· W143540889 on OpenAlexaffvenue
Gaëtane Jean‐Marie, Steve Sider

Bibliographic record

VenueComparative and International Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEducational leadershipLeadership studiesContext (archaeology)Political scienceState (computer science)Leadership stylePublic relationsPsychological resilienceInstructional leadershipSociologyPedagogyPsychologySocial psychologyGeography

Abstract

fetched live from OpenAlex

Although there has been extensive examination of educational leadership in the developed world (e.g. Fullan, 2001; Leithwood & Sun, 2012), there has been much less research on school leadership in fragile states such as Haiti. This paper responds to Dimmock and Walker’s (2000) call for greater attention to comparative and international research on educational leadership specifically by examining school leadership in the Haitian context. The study on which this paper is based examines the experiences of eight school leaders in Haiti in response to the question: What types of leadership practices do school leaders in Haiti exhibit? Three themes are presented: responsiveness to localized needs, a commitment to educational change and improvement, and innovation in responding to challenging contexts. We discuss how these themes may be illuminating of school leadership in fragile states by considering communal and community-based leadership, resilience, and the momentum for change in consideration of Moorosi and Bush’s (2011) work on localized networks for change.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.005
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.344
GPT teacher head0.439
Teacher spread0.095 · 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 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

Citations7
Published2014
Admission routes2
Has abstractyes

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