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Record W3087217440 · doi:10.11575/prism/38751

High School Redesign: Carnegie Unit as a Catalyst for Change.

2020· article· en· W3087217440 on OpenAlexaffabout
Barbara Brown, Gabriela Alonso-Yañez, Sharon Friesen, Michele Jacobsen

Bibliographic record

VenuePRISM (University of Calgary) · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUnit (ring theory)Focus groupDispositionMathematics educationMultimethodologyQualitative propertyKnowledge managementMetric (unit)PsychologyPedagogySociologyComputer scienceEngineeringOperations management

Abstract

fetched live from OpenAlex

Researchers examined seven schools in Alberta undergoing high school redesign and removing the Carnegie Unit, a time-based metric for awarding course credits. A mixed methods convergent parallel design was used to gather data from leadership teams in the schools and to examine evidence of impact on student learning. Qualitative and quantitative data were analyzed concurrently and then merged for the analysis. Findings illustrate that removing the Carnegie Unit was a catalyst for redesign and learning improvements. Five constitutive factors enable high school redesign, including a collective disposition as a learning community, a focus on relationship building, obtaining student input, collaboration, and making changes to learning tasks and assessment practices. The findings provide insight into the ways in which leadership teams formed complex adaptive systems to enable change and may serve to inform practitioners and school leaders, schools and systems, and those who study policy changes in 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 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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.716

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.003
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.321
Teacher spread0.229 · 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 designNot applicable
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

Citations1
Published2020
Admission routes2
Has abstractyes

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