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Record W1886218056 · doi:10.26522/brocked.v15i2.76

Harbors of Hope: The Planning for School and Student Success Process

2006· article· en· W1886218056 on OpenAlexvenueaboutno aff
Sonya Pancucci

Bibliographic record

VenueBrock Education Journal · 2006
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueProcess (computing)Professional developmentPedagogySociologyPublic relationsPsychologyMedical educationPolitical scienceManagementMedicine

Abstract

fetched live from OpenAlex

Hope, schools, professional learning communities,and school improvement planning – what links these words? According to Hulley and Dier (2005), hope is the key to achieving successful and effective schools through reculturing with professional learning communities as the vehicle for change in the school improvement process. Wayne Hulley, president of Canadian Effective Schools Incorporated and senior consultant for the Franklin Covey Company, has 35 years of experience in North America working in the area of school improvement. Co-author Linda Dier has extensive knowledge having worked for 30 years in education systems in Manitoba and Saskatchewan. Currently, she is senior consultant with Canadian Effective Schools Inc. and administrator of the Canadian Effective Schools League. Together, Hulley and Dier have written a text for educators and administrators at the district, board, and school levels, combining research theory with the practical knowledge gained in their joint 70+ years’ experience in education to provide a comprehensive planning process for school improvement. This text presents a step-by- step process that notes the highs and lows or « implementation dips » of the school improvement cycle. The authors have utilized the learning community model of professional development as a vehicle to facilitate, guide, direct, and sustain change towards successful and effective schools.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.438
Teacher spread0.408 · 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 designObservational
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

Citations12
Published2006
Admission routes2
Has abstractyes

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