Учещите се градове в контекста на концепцията за учене през целия живот (визия на студентите, изучаващи учебната дисциплина „Андрагогия“)
Bibliographic record
Abstract
В студията са очертани характеристиките на учещите се градове (в т.ч. на единствения учещ се град в България - Пловдив) и ролята им за разширяване на възможностите за учене на възрастните с цел повишаване качеството на живота им, както и на живота в общността. Генерирани са препоръки за развитие на идеята за учещите се градове в България. Анализирани и обобщени са вижданията на студентите от Факултета по педагогика, изучаващи учебната дисциплина „Андрагогия“, за задължителните елементи от структурата на учещия се град и дейностите, извършвани в него. Разгледани са възможностите, които предлагат за професионалната реализация на студентите от специалност Неформално образование. Библиография: Божилова, В. (2017) Обучение на възрастни. Концепции, методически насоки, практически решения. София. Божилова, В. (2015) Образованието на възрастни и учещите се градове. София. Гюрова, В. (2011) Андрагогията в шест въпроса. София Михайлова-Недкова, Г. (2021) Съвременни форми на обучение и развитие в креативност със студенти - педагози в дигитална среда. Плевен Bengtsson, J. (2013) National strategies for implementing lifelong learning - The gap between policy and reality: An international perspective. International Review of Education, 59 (3), p. 343-353. Brown, J. S. & Duguid, P. (2000) The social life of information. Boston, Massachusetts: Harvard Business School Press. Carlsen, A. (2013) The future of lifelong learning. International Review of Education, 59 (3), p. 311-318. Commission of the European Union (2003) R3L programme https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2003:0068:FIN:EN:PDF (14.01.2023 r.) Crowley, L., N. Cominetti (2014) The geography of youth unemployment: A route map for change. Lancaster, The Work Foundation. DfEE (1998) Learning Towns, Learning Cities - The Toolkit - Practice, Progress and Value. Sudbury: DfEE: http://www.lifelonglearning.co.uk/learningcities/ (12.02.2023). Faris, R. (2007) Learning Communities: Webs of Life, Literacy and Learning, Halifax, Symposium on Learning Communities. Gongalves, M. J. (2007) Portuguese Entrepreneurial Women: fostering learning communities, Lifelong Learning in Europe, Volume XXII, issue 2, p. 82-89. Granito, V. J., M.E. Santana (2016) Psychology of Learning Spaces: Impact on Teaching and Learning, Journal of Learning Spaces, Volume 5, Number 1. Kearns, P., R. McDonald, P. Candy, S. Knights, & G. Papadopoulos (1999) VET in the learning age: The challenge of lifelong learning for all, Vol. 2: Overview of international trends, and case studies). Canberra: National Centre for Vocational Education Research Ltd. Longworth, Norman (2003) Lifelong Learning in Action: transforming education in the 21st century, London: Kogan Page. Longworth, N. and Osborne, M. (2010) Six ages towards a learning region: A retrospective. European Journal of Education, 45 (3), p. 368-401. NIACE (1997) The learning divide: a study of participation in adult learning in the United Kingdom, NIACE. Osborne, M. (2003) University continuing education - International understandings. In: M. Osborne and E. Thomas. eds. Lifelong Learning in a Changing Continent: Continuing Education in the Universities of Europe. Leicester, NIACE. Regmi, K. D. (2015) Lifelong learning: Foundational models, underlying assumptions and critiques. International Review of Education, p. 135-145. Rogers, A. (2006) Escaping the slums or changing the slums? Lifelong learning and social transformation. International Journal of Lifelong Education, 25 (2), p. 125-137. Saepudin, A., D. Mulyono (2019) COMMUNITY EDUCATION IN COMMUNITY DEVELOPMENT B: Jurnal Empowerment Volume 8 Nomor 1, Universitas Pendidikan Indonesia, Siliwangi, p. 2580-7692. Sanky, K. and Osborn, M. (2006) „Lifelong Learning region where other learning doesn’t reach“, in R. Edwards et al Researching learning outside the academy. London: Routledge Soczka, L. (2005) Viver (Na) Cidade in Soczka. L. (Org.), Contextos Humanos e Psicologia Ambiental, Lisboa: Fundagao Calouste Gulbenkian, p. 91-131. Tibbitt, J. (2018) A benchmarking approach to understanding community engagement and learning cities. PASCAL Briefng Paper 17. UNESCO Institute for Lifelong Learning (UIL) (2021) Inclusive lifelong learning in cities: Policies and practices for vulnerable groups, Hamburg. UIL (2011) Conceptual evolution and policy developments in lifelong learning. Hamburg, UIL. P. 4-46.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".