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Record W628957319

Measuring what matters competency-based learning models in higher education

2001· book· en· W628957319 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBibliothèque et Archives nationales du Québec (Québec government) · 2001
Typebook
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsService-learningExperiential learningService (business)Variety (cybernetics)SociologyPublic relationsPsychologyPedagogyPolitical scienceComputer scienceBusinessArtificial intelligenceMarketing
DOInot available

Abstract

fetched live from OpenAlex

EDITORS' NOTES (Mark Canada, Bruce W. Speck). 1. Why Service--Learning? (Bruce W. Speck). Service--learning is generally based on one of two impulses, philanthropic or civil, each with its own distinct philosophical viewpoint. Teachers should be aware of these impulses as well as the major challenges of service--learning. 2. A Smart Start to Service--Learning (Maureen Shubow Rubin). A seven--step model can help newcomers develop a successful service--learning course. 3. Service--Learning Is for Everybody (Robert Shumer). A variety of strategies can help service--learning faculty reach out to include more people with disabilities as providers of service. 4. Creating Your Reflection Map (Janet Eyler). A systematic approach to encouraging reflection can help students get the most out of service--learning courses. 5. The Internet in Service--Learning (Mark Canada). Students can serve their communities by helping agencies create World Wide Web sites and by building university--based Internet resources. 6. A Comprehensive Model for Assessing Service--Learning and Community--University Partnerships (Barbara A. Holland). A global approach to assessing service--learning initiatives provides data to demonstrate that learning is taking place and to refine these initiatives so that they can be even more successful in the future. 7. The National Society for Experiential Education in Service--Learning (Lawrence Neil Bailis). Professors do not have to reinvent the wheel when they teach service--learning courses. The National Society for Experiential Education provides a variety of resources to help both novices and veterans succeed. 8. Advancing Service--Learning at Research Universities (Andrew Furco). Despite their emphasis on scholarship, research universities are appropriate places to use service--learning. Three strategies can help practitioners overcome obstacles. 9. How Professors Can Promote Service--Learning in a Teaching Institution (Kathy O'Byrne). Although a college devoted to teaching seems the ideal place to promote service--learning, faculty at such institutions should actively seek key stakeholders' support to ensure that service--learning thrives. 10. Humanistic Learning and Service--Learning at the Liberal Arts College (Edward Zlotkowski). Faculty at liberal arts colleges can take advantage of their institution's mission in order to promote service--learning. 11. Additional Resources (Elaine K. Ikeda). A number of core resources can help faculty begin or improve service--learning at their institution. INDEX.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.253
Teacher spread0.208 · 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