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Record W2070716600 · doi:10.1207/s15430421tip4502_2

Organizational Learning in Schools and School Systems: Improving Learning, Teaching, and Leading

2006· article· en· W2070716600 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTheory Into Practice · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationSchool systemPedagogyPsychologyOrganizational learningSociologyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

Abstract Teachers and administrators in school systems across the United States and Canada have been inundated during the last several decades with silver bullets and quick fixes in an effort to improve education. The reforms have had limited success and little long-term effect on schools and school systems. This article proposes that organizational learning (ongoing learning in a deliberate manner with a view to improvements supporting the organization's goals) has the potential to help schools and school systems renew themselves from the inside out and to improve learning (for students and adults), teaching, and leading in school systems. The authors identify 6 interrelated conditions that appear to foster organizational learning and provide a practical illustration of the conditions in the form of a fictional school created from examples in the literature

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.

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)0.013
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.365
Teacher spread0.344 · 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