Finnish Students’ Educational Provision Experience Towards Resilience, Recovery and Renewal of Education Systems
Bibliographic record
Abstract
The aim of this study is to learn in what ways has the COVID-19 pandemic influenced young people's educational experiences, psychosocial well-being, and engagement with traditional and local practices from its onset to the recovery phase in Lapland, Finland. This study is conducted in REAP - Resilient Experiences and Agency of Youth and Children During the Pandemic: Re-visioning Education through Storytelling - project that compares the experiences of young people in Canada, Finland, and the UK. In this study, the responses of the young people from Finland are investigated. In order to meet the current changing demands of society and enterprises, reformation of the educational system is needed. Deep learning theory (Fullan et al., 2017) provides a four-layer framework to generate the set of six global competencies that are essential for learners in their future working life. Furthermore, radical collegiality (Fielding, 1999) highlighted the focal role of deeper engagement beyond student voice and student agency to empower the new learning partnership with teachers, families and communities. Together, they plait a well-done braid to form a theoretical foundation for this study. By combining both quantitative descriptive analysis and qualitative thematic analysis, this exploratory mixed methods study examines the responses of 116 secondary students studying in Finland. The findings can provide insights into how these experiences can foster resilience, support recovery, and drive the renewal of education systems by enhancing educational delivery, promoting psychosocial well-being, and leveraging local and traditional knowledge.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".