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Record W4389198147 · doi:10.21125/iceri.2023.1579

COIL PROJECTS AS A MEANS TO FOSTER COLLABORATIVE WORK IN THE PROFESSIONS

2023· article· en· W4389198147 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueICERI proceedings · 2023
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsAlgoma University
Fundersnot available
KeywordsWork (physics)Library scienceEngineeringSociologyPolitical scienceEngineering ethicsComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Collaborative Online International Learning (COIL) projects aim at achieving a common goal through teamwork and cooperation. Collaborative skills are among the top soft skills employers want from their employees. COIL as a teaching and learning method develops reflexivity skills since at the core of a COIL experience students are asked to examine their own reactions and motives to face specific course contents (such as ways to feel about and act to find solutions for social and environmental issues). Likewise, instructors face the challenge of imparting awareness leading to cultural shifts creating spaces where students discuss and analyse how different groups think or act in the same situation. This particular COIL experience brought together two universities, one located in Canada and the other in Spain, both including students from different ethnic and cultural provenance.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.033
GPT teacher head0.296
Teacher spread0.263 · 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