Bibliographic record
Abstract
In the first section of this volume, authors outline the respective national challenges for civic educators in the U.S., England, Australia, and Hong Kong.Their conclusions are based on the results of the International Association for the Evaluation of Educational Achievement Study (otherwise called the "IEP" or "CivEd" Study).In the second part of the volume, authors who have conducted qualitative research offer conclusions intended to help educators improve the quality of citizenship education in Europe and Germany.The cross-section of results and interpretations presented on the status of citizenship education are both disparaging and illuminating.Part I: Interpretations of quantitative studies into citizenship knowledge and attitudes In Chapter One, Hahn outlines the national and comparative status of early teen readiness for citizenship.She reports that: "…at age 14 most students in the United States have a good general understanding of democracy and democratic principles, and they report a number of attitudes and behaviours that point toward their becoming civically engaged, tolerant citizens" (p.17).Hahn emphasizes that this knowledge is basic and insufficient insofar as it could lead to engagement in political processes such as voting or interest in and discussion of controversial public issues.Because students only have a cursory understanding of national government, they "…are being inadequately prepared to deal with international issues" (pp.23-24).Hahn offers concrete suggestions for classroom improvements that can help address the challenges she thinks civic educators face in a post 9/11 era.Similarly, in Chapter Two, Kerr highlights the issue of students' lack of in-depth knowledge of democratic processes and practices, particularly with respect to elections and again, participation in political activities (p.34).One reason Kerr offers for the break between procedural understanding and action is that "It suggests that students have had limited opportunities to learn about , experience and understand these aspects of civic and political society, either in school or in the communities they live" (p.34).Kerr suggests that another cause for the disconnection between the understanding of democratic ideals and political action is the general mistrust or negative perceptions students have of government institutions.In this piece, Kerr provides a list of guiding questions he developed in response to the study results that he hopes will help educators identify early their agendas for developing citizenship education.In answering these questions, we may be able to encourage both a depth of understanding of political process and more student engagement in "effective" political action (p.36).In Chapter Three, Kennedy and Mellor raise nuanced points about the IEP study itself and the nature of citizenship knowledge.First, they reiterate the
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".