Using Critical Discourse Analysis to Address the Gaps, Exclusions and Oversights in Active Citizenship Education
Bibliographic record
Abstract
There is great concern that active citizenship policies, curriculum and/or pedagogy are not working effectively, and many researchers are seeking ways to engage students more in public affairs and political realities. In this dissertation, I explore the captivating universe of active citizenship education and the discourses that propel it, using critical theory, documentary method and critical discourse analysis. I analyze over 400 documents that directly or indirectly relate to citizenship education to determine where we have been, where we currently are, and where we ought to go with active citizenship education. As a result of my research, I discovered that the discourses that originally constructed notions of the citizen, citizenship and the rights of the citizen (e.g., Socrates, Michavelli, Rousseau, Jefferson, Roosevelt, and Martin Luther King, Jr.) differed, but they often shared themes of self-reflection, critique, and emancipation. Unfortunately, these fundamental pillars fell increasingly by the wayside when, for example, globalization spun its web (e.g., mass migration, the Internet, access to faster systems of travel and free trade). Correspondingly, neoliberal discourse penetrated local, state and global systems, and citizenship education like many other aspects of society was altered. A newly designed ‘global’, ‘unregulated’, ‘knowledge society’ claimed a new vision for civil society and thus citizenship and citizenship education. Such discourses became imbedded not only in the corporate world but also in public institutions – education was not immune to this. I discovered that although universal discourses such as sustainability, cooperation, and human rights are promoted in secondary citizenship education via social studies curriculum, little of this discourse is instituted in policy, curriculum and pedagogy. Students are not being given many classroom opportunities to become reflective, engaged and empowered citizens with the capacity to shape society and challenge institutionalized oppressions such as racism, poverty, sexism, ageism, and classism. Based on moral, ethical and democratic imperatives, I present recommendations on how to move forward to create the citizenship education programs youth deserve. I provide guiding principles, a navigational illustration, and an exemplar of what a revised citizenship education curriculum might look like.
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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 | 0.147 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.023 | 0.046 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| 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".