Linking lifetimes : a global view of intergenerational exchange
Bibliographic record
Abstract
Chapter 1 Preface Chapter 2 Conceptual Issues: A Conceptual Framework for Cross-Cultural Comparisons of Intergenerational Initiatives Challenging Intergenerational Stereotypes Across Eastern and Western Cultures Strengthening Intergenerational Bonds through Volunteerism: A G Chapter 3 National and Regional Profiles: North America: Advancing an Intergenerational Agenda in the United States Intergenerational Teaching and Learning in Canadian First Nations Partnership Intergenerational and Possibilities in Chapter 4 Pacific & Asian Region: Intergenerational Initiatives in Singapore: Commitments to Community and Family Building Intergenerational Initiatives in the Marshall Islands: Implications for Promoting Cultural Continuity Intergenerational in Ja Chapter 5 Europe: Intergenerational Community Building in the Netherlands Chapter 6 Intergenerational Engagement in the UK: A Framework for Creating Inclusive Communities German Pupils and Jewish Seniors: Intergenerational Dialogue as a Framework for Healing History Chapter 7 Latin America: Intergenerational Relationships in Latin America and the Caribbean Cuba: Fertile Ground for an Intergenerational Arts Movement Chapter 8 South Africa: Intergenerational Initiatives in South Africa: Reflecting and Aiding a Society in Transition Chapter 9 Time to Organize: Organizing at the National Level: Lessons Learned from the U.S. and Japan Creating an International Consortium for Intergenerational Programs
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".