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Record W2202956154 · doi:10.1080/17508487.2016.1117004

Teachers’ work and innovation in alternative schools

2015· article· en· W2202956154 on OpenAlexaffabout
Nina Bascia, Rhiannon M. Maton

Bibliographic record

VenueCritical Studies in Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsMainstreamCurriculumAlternative educationSociologyWork (physics)PedagogyCurriculum developmentReform movementDemocracyPublic relationsHigher educationEducationMathematics educationPolitical sciencePsychologyEducation policyPoliticsEngineering

Abstract

fetched live from OpenAlex

Toronto boasts a large and diverse system of public alternative schools: schools where democratic practices, student access and a commitment to public education are fundamental. There are academic schools; schools with thematically focused curricula; schools driven by social movement principles such as antiracism and global education; schools for students who do not thrive in mainstream schools; and schools with alternative scheduling and delivery practices for students who must work. The schools are small, supporting personalized relationships among teachers and students, with teacher-driven curricular programs that are responsive to student interests. Curricular innovation is made possible because alternative schools are only loosely coupled with the rest of the public education system, but they still must comply with school system regulations. This paper describes how teachers’ work and the structural elements of alternative schools support school-based innovation.

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.051
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.188
GPT teacher head0.489
Teacher spread0.301 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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