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Record W4399765071 · doi:10.1515/9780776625843-005

3 Teaching and Assessment with the CLB: Teacher Experiences and Perspectives

2017· book-chapter· en· W4399765071 on OpenAlexaffabout
Eve Haque, Antonella Valeo

Bibliographic record

VenueUniversity of Ottawa Press eBooks · 2017
Typebook-chapter
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

T he importance of classroom-based official-language instruction for newcomers cannot be overstated.Almost half of the immigrants surveyed as part of the Longitudinal Survey of Immigrants to Canada (Chui 2003) participated in adult immigrant English-language training and, of this group, 85 percent found English-language training classes to be useful or very useful (Smit and Turcot 2010).Thus, the development of the Canadian Language Benchmarks (CLB) is arguably one of the most significant endeavours to come out of the publicly funded arena of English as a second language in Canada.As such, the importance of the CLB for programming and instruction in these classes across Canada cannot be underestimated.Although described as a "foundation of shared philosophical and theoretical views on language ability that informs language instruction and assessment" (CIC and CCLB 2012, v), it is how the CLB ultimately inform language instruction in the classroom that is the focus of this chapter.The CLB are clearly labelled as a set of descriptive statements about communicative competencies and levels on a continuum of language ability, and a standard and reference framework for planning curriculum for teaching and learning.As well, it is clearly laid out what the CLB are not; that is, not a curriculum, instructional method, or assessment, nor are they a description of the discrete elements of knowledge and skills underlying communicative competence, such

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.035
GPT teacher head0.229
Teacher spread0.194 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes2
Has abstractyes

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