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Record W2112793374 · doi:10.26522/tl.v4i1.10

Coffee and Collaboration: A Tean Approach to Talking Learner Challenges

2007· article· en· W2112793374 on OpenAlexaffvenueabout
Shirley Kendrick, Max Vecchiarino

Bibliographic record

VenueTeaching and Learning · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of VictoriaBrock University
Fundersnot available
KeywordsExcellenceContext (archaeology)Christian ministryLiteracyPedagogyPsychologyMathematics educationPublic relationsPolitical scienceGeography

Abstract

fetched live from OpenAlex

The focus of this issue of Teaching and Learning, "Boys and Literacy," is an example of an education concern defined within the context of "authentic inclusive schooling and excellence for all" as defined by the Ontario Ministry of Education. Professionals interested in improving achievement and performance objectives related to student and school based learning and who regularly seek out opportunities to engage in group discussion and collaboration are often able to bring about change within the education environments they are employed. In the instance at hand, boys and literacy it is now more fully understood that beside planning for the host of learner contingencies that contribute to an individual learner profile, gender and socio economic influences and or differences need to be understood within the context of identifying learner challenges and needs. And that they be interpreted and represented in terms of successful classroom practices.

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)0.022
metaresearch head score (Gemma)0.026
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.003
Science and technology studies0.0250.035
Scholarly communication0.0260.028
Open science0.0070.026
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0100.002

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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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

Citations0
Published2007
Admission routes3
Has abstractyes

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