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Record W2095936276 · doi:10.29173/cmplct8791

A New Science Look at Negotiating Curriculum and Classrooms

2008· article· en· W2095936276 on OpenAlexvenueno aff
Perrin Blackman

Bibliographic record

VenueComplicity An International Journal of Complexity and Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMathematics educationPaceClass (philosophy)PedagogyNarrativeNegotiationGrading (engineering)Computer sciencePsychologySociologyLinguisticsEngineering

Abstract

fetched live from OpenAlex

Chaos and complexity theories provide a new perspective on curriculum design and application, especially in the area of Teaching English as a Second Language. Since language learning is a fluid process, teachers need to be able to react in real time to factors beyond their control. Rather than seeing these factors as interruptions to a closed system, teachers can make decisions about what to focus on and what to gloss over, when to pick up the pace and when to slow down, finding a natural rhythm that helps to create a symbiotic relationship with the students, the classroom and the university or school structure. Time and identity figure largely in the ESL classroom, and as each semester progresses, an essential layering process occurs and narrative structures are built. As language skills develop and as the classroom takes form, personalities are defined and redefined not only for the students but for the class as a whole. This paper attempts to make new science theories more accessible to ESL teachers with the hope that they can describe and discover ways to allow for necessary classroom flexibility while also respecting conventional curriculum standards and outcomes.

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)0.005
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.016
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.046
Scholarly communication0.0160.034
Open science0.0020.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0100.001

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 head0.075
GPT teacher head0.380
Teacher spread0.304 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueComplicity An International Journal of Complexity and EducationSame topicEducation and Critical Thinking DevelopmentFrench-language works237,207