Citizenship - from ideal to reality. \nWhat are the perceptions of Citizenship Education's curricular delivery in schools?
Bibliographic record
Abstract
For the last ten years, Citizenship Education has maintained its place on the National Curriculum as a statutory subject. This positioning has been fraught with difficulties as teachers struggle to know how the concepts of ‘Identity and Diversity’, ‘Rights and Responsibilities’ and ‘Democracy and Justice’ should best be ‘taught’, if at all. It is argued that the transition of Citizenship Education from policy to the delivered curriculum has been, and still is, highly problematic due to a historic mismatch between the priority it is given at policy level and the very different priority it is given in schools. It is argued that this mismatch is a result of inadequate resourcing (time, knowledge, human) and has implications for teacher and student perception of the subject and a consequently detrimental effect on its potential success. It is further argued that according to McCowan’s (2006) ‘curricular transposition’ model, attention must be given to the areas of implementational difficulty which Heater (2001) specifies and are corroborated as still existing 10 years on by empirical data collected for this study. The research identifies and labels three key school delivery models of Citizenship Education (Combined, Discrete and Integrated). It makes a comparison between the effectiveness of these models and looks at the varying impact of these models in terms of teacher and student perception. The study concludes that the enthusiasm of the students is largely dependent on that of the teachers, who value the subject, but need support and clarity from the government and from school management, in order to effectively and confidently deliver the Citizenship Education curriculum. The tension between vision and pragmatism is still very strong, but would be eased by more investment of thought and resources. If this investment is not made, then expectations about the impact of the subject need to be lowered.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".