Youth Work and Schools in ‘Full-day’ education systems: International Comparison of Links between Formal and Non-formal Education
Bibliographic record
Abstract
In this article I will outline the methodological approach of a non-empirical comparative research project which I began in 2003. The project is situated in the context of the research training group"Youth Welfare in Transition" at the universities of Bielefeld and Dortmund, funded by the German Research Council (Deutsche Forschungsgemeinschaft). In that context I have organised an international conference about the modes of cooperation between school and youth work agencies with colleagues from Canada, France, Finland, Italy, Japan, the Netherlands, Russia, Switzerland, the United Kingdom, the United States, Israel, and Germany. Meeting in Bielefeld from the 9th to the 11th of October 2003, we compared the respective national arrangements of formal and non-formal education (www.uni-bielefeld.de/paedagogik/agn/ag8/Ganztagsbildung.html). This note is based on the scheme of comparison which was given to the contributors in order to help them preparing their presentations. At the moment the scheme is nearing completed with significant data prepared by the contributors/authors (see Otto/Coelen 2004), supplemented with data from research works published in German and English. The next step will be to set up an empirical project about the relationships between schools and youth work agencies in three European countries (probably France, Finland and the Netherlands).
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".