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Record W4221039608 · doi:10.5539/ies.v15n1p210

A Mission Statement Does Not a Mission Make: A Mixed Methods Investigation in Public Education

2022· article· en· W4221039608 on OpenAlexvenueno aff
David C. Coker

Bibliographic record

VenueInternational Education Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Strategy and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsMission statementThematic analysisConformityQualitative researchPedagogyPsychologySociologyMultimethodologyPublic relationsPolitical scienceSocial psychologySocial science

Abstract

fetched live from OpenAlex

Public schools widely use mission statements, and many educational administration programs teach mission statements as a necessary lever for school improvement. A mixed methods investigation examined three levels. An experiential phenomenological analysis examined graduate students’ experiences with mission statements within their own schools and professional life. A thematic analysis examined 80 schools in the Midwestern United States, broken down by high and low performance on state academic testing, ecological differences, quantitative structures of the mission statement, and qualitative themes and dimensions. A meta-synthesis compared findings with previous research. There were structural differences in mission statements, but the conclusion was mission statements were a legacy practice which served the political spectacle, and practitioners adopted the practice out of conformity. There was no direct evidence mission statements achieved the stated purpose. Recommendations were made to refashion mission statements and the school improvement process around four factors.

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)0.129
metaresearch head score (Gemma)0.105
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 score0.129
Threshold uncertainty score0.682

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1290.105
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0050.004
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.389
Teacher spread0.313 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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