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Record W2105170012 · doi:10.22230/ijepl.2008v3n7a127

Administrator Perceptions of School Improvement Policies in a High-Impact Policy Setting

2008· article· en· W2105170012 on OpenAlexvenueno aff
Mario Torres, Luana Zellner, David A. Erlandson

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityLikert scalePerceptionScale (ratio)SchedulePsychologyPublic relationsProfessional developmentPolitical sciencePedagogyEconomicsGeographyManagement

Abstract

fetched live from OpenAlex

This study investigated school administrators’ perceptions of school improvement policies in a high-impact policy environment by measuring the impact of accountability, site-based management, professional development, and scheduling reform on the three dependent variables of a) academic outcomes, b) staff morale, and c) parent and community involvement. Using a convenience sampling method, 49 public school principals from Texas participated and an online survey was constructed to gather both quantitative (i.e., Likert scale) and qualitative (i.e., open ended response) data. The findings clearly point to principals, regardless of geographical district type and grade level school type, viewing less controversial and more intrinsically oriented policies (i.e., site-based management and professional development) as having a greater positive impact on outcomes as a whole than more radical alternatives (i.e., accountability and time and schedule reform). The evidence suggests that more aggressive school improvement policy approaches are likely failing to generate enough convincing outcomes to generate high commitment and confidence from school leaders. Further studies may look at the interaction of policy impact with minority student enrollments and with subgroup populations.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.154
GPT teacher head0.449
Teacher spread0.294 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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