Knowledge that Counts in a Global Community: Exploring the Contribution of Integrated Curriculum
Bibliographic record
Abstract
As the third millennium progresses, we are faced with increasing pressures relating to climate change and the sustainability of life on Earth. Concerned citizens are realizing that the responsibility to respond is both local and global. There is an increasing sense of urgency about the need to reform the processes of schooling and curriculum to better prepare students for global citizenship. Educators, policy makers and the wider community are seeking information about how to proceed with this reform effort, particularly how alternative and integrated approaches to curriculum can be used to engage students with the important issues of our time. \n \nKnowledge that Counts in a Global Community explores the potential contribution of curriculum integration in a context where school curricula are typically segregated by discipline. It offers curriculum integration as a powerful tool for educating young citizens so that they can understand and respond to global concerns. It argues for an informed citizenry who can think broadly across disciplines, and contribute sensibly and pragmatically to local problems with an eye on how this translates to making a global difference. In its examination of the twin themes of global knowledge and curriculum integration, the book explores: \n \nthe nature of curriculum integration \nthe nature of knowledge \nthe nature of learning \nThe authors reflect on these issues from perspectives gained by more than a decade of research in the area. Their in-depth, scholarly exploration and critical analysis of current approaches to curriculum, introduces educators and academics to contemporary ways of conceptualizing the complexities of, and relationships among curriculum integration, knowledge and learning. Throughout the book, the authors emphasize the central curriculum question, what kinds of outcomes do we want for students of the twenty-first century? \n \nThis book will provide a valuable resource for academic educators, researchers, teachers and others interested in educational policy reform.
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 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.054 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".