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Record W2138440028 · doi:10.15365/joce.0803022013

Innovation in Educational Markets: An Organizational Analysis of Private Schools in Toronto

2005· article· en· W2138440028 on OpenAlexafffundabout
Scott Davies, Linda Quirke

Bibliographic record

VenueCatholic education/Catholic education (Dayton, Ohio. Online) · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsMcMaster University
FundersMcMaster UniversityUniversity of Notre Dame
KeywordsIsomorphism (crystallography)AccountabilityPrivate sectorSchool choiceSociologyOrganizational theorySPARK (programming language)Qualitative researchPublic relationsEconomicsPolitical scienceManagementEconomic growthMarket economySocial science

Abstract

fetched live from OpenAlex

This study examines whether new private schools are innovative, drawing on theories of markets and institutions. Choice advocates claim that markets spark innovation, while institutional theory suggests that isomorphic forces will limit novel school forms. Using qualitative data form third sector private schools in Toronto, three hypotheses about the impact of markets on educational organization are examined: (a) they reverse tendencies toward isomorphism as schools develop client niches; (b) they allow schools to weaken their formal structures; and (c) they force schools to more closely monitor their effectiveness. Substantial evidence exists for the first hypothesis, partial evidence for the second hypothesis, but little evidence for the third. Overall, new private schools are characterized by: small classes, unique pedagogical themes, personalized treatment of clients, and some pragmatic responses to limited resources. Their operators sometimes feel restricted by parental demand, but are able to retain a loosely coupled structure by embracing consumerist understanding of accountability. This essay concludes with a discussion if implications for market theory.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.009
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.016
GPT teacher head0.358
Teacher spread0.341 · 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

Citations3
Published2005
Admission routes3
Has abstractyes

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