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Record W1560007169 · doi:10.26522/brocked.v19i2.131

Teachers as Bridges: Educating for a Better Tomorrow

2010· article· en· W1560007169 on OpenAlexvenueno aff
Julian Kitchen, Darlene Ciuffetelli Parker

Bibliographic record

VenueBrock Education Journal · 2010
Typearticle
Languageen
FieldComputer Science
TopicEducational Challenges and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsVitalityPedagogyProfessional developmentSociologyBridge (graph theory)PsychologyMathematics education

Abstract

fetched live from OpenAlex

Teachers act as bridges for their students. Teachers link young individuals to the knowledge and traditions of society. Through their professional practice and dedication, they help students develop the skills to succeed in school and in the world. In their classes, teachers help students relate to each other across cultures, which is crucial to the development and vitality of civil society in a changing world. Dedicated teachers are bridges reaching out to exceptional students or to underappreciated subjects. Visionary teachers develop innovative approaches that offer hope of a brighter future for everyone. The articles in this issue of Brock Education all highlight different ways in which educators are bridges to a better tomorrow.

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)0.006
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 score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.007
Scholarly communication0.0130.022
Open science0.0010.013
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0180.004

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 head0.021
GPT teacher head0.320
Teacher spread0.299 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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