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Record W3173849181 · doi:10.7202/1078518ar

Emergence in School Systems: Lessons from Complexity and Pedagogical Leadership

2021· article· en· W3173849181 on OpenAlexaffvenueabout
Gabriela Alonso-Yañez, A. Paulino Preciado-Babb, Barbara Brown, Sharon Friesen

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsScholarshipEducational leadershipContext (archaeology)PedagogyLeadership stylePopulationSociologyEmpirical researchMathematics educationPsychologyPolitical scienceSocial psychologyGeography

Abstract

fetched live from OpenAlex

The theoretical framework for this study draws on conceptual advances from two bodies of scholarship: 1) complexity thinking in education, which has recently focused on school system change and, 2) school leadership research, which has recently attended to the effects of leadership interventions to school improvement. Using a complexity-thinking framework, the purpose of this study was to understand how leadership practices contribute to shaping change in school systems and how change occurred across the system. Our study was conducted in an urban centre in Alberta within a public-school jurisdiction and in an area of the city that had a high population of students from culturally and linguistically diverse backgrounds from low-income households compared to other areas across the school jurisdiction. Students in this area typically scored in the lowest quartile on provincial standardized examinations. Our findings are significant because complexity thinking in the context of school leadership has not received sufficient empirical attention. In our study we identified and described pedagogical leadership practices that play a central role in redressing disparities currently found 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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.598
GPT teacher head0.551
Teacher spread0.047 · 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.

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

Citations10
Published2021
Admission routes3
Has abstractyes

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