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Foreign Languages and Sustainability: Addressing the Connections, Communities, and Comparisons Standards in Higher Education

2010· article· en· W2158526459 on OpenAlexaff
Eleanor ter Horst, Joshua M. Pearce

Bibliographic record

VenueForeign Language Annals · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsGermanSustainabilityForeign languageSustainable developmentUnit (ring theory)Service-learningPedagogyEnvironmental educationComputer sciencePolitical scienceMathematics educationSociologyKnowledge managementPsychologyLinguisticsEcology

Abstract

fetched live from OpenAlex

Abstract: This article describes an interdisciplinary collaboration that combined the study of German language with instruction in environmental issues (sustainable development). The project, involving both an independent study and a classroom unit, allowed students to make connections between disciplines, establish contact with German‐speaking communities outside the university, and make cultural and linguistic comparisons. By expanding the German‐language content on the Web site Appropedia.org, which is devoted to global sustainable development, students took an active role in learning by creating content that can be read and used by the global community of German speakers. This project provided a model for successful interdisciplinary instruction. The results of this study show that integrating environmental issues with foreign language study provides significant opportunities for students to increase their language proficiency, develop their understanding of concepts related to the environment, and become more involved in a global community through a virtual service learning project.

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)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0080.004
Open science0.0010.012
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.087
GPT teacher head0.412
Teacher spread0.325 · 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
Published2010
Admission routes1
Has abstractyes

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