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Record W3184453993 · doi:10.22230/ijepl.2021v17n7a1103

A Psychometric Look at Principal Professional Development

2021· article· en· W3184453993 on OpenAlexvenueno aff
Lee A. Westberry, Zhao Fei

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyProfessional developmentPrincipal (computer security)Leadership developmentRank (graph theory)Educational leadershipLeadershipLeadership styleMedical educationPedagogyPolitical sciencePublic relationsSocial psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

This study evaluates aspects related to P12 principals’ professional development needs in South Carolina regarding the three domains of school leadership: management, instructional leadership, and program administration. A survey to rate principals’ current leadership knowledge, rank order their professional development needs, and provide a confidence rating regarding their abilities was given to over 1,100 principals and 85 superintendents. Through examining relationships with a psychometric model, results derived latent leadership ability scores and self-reported confidence ratings of principals as well as the superintendents’ leadership scores and confidence ratings of their principals. This study found a significant discrepancy between principals’ and superintendents’ confidence ratings and their corresponding leadership ability scores, respectively. A further analysis of the rank-ordered professional development needs highlighted instructional leadership to be the most needed topic for professional development. Finally, atypical response patterns regarding principal’s current leadership knowledge are also identified through person-fit analysis to provide additional information regarding P-12 principals’ professional development needs.

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)0.016
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.307
GPT teacher head0.472
Teacher spread0.165 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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