Finnish pre-service teachers’ knowledge about the Indigenous Sámi people
Bibliographic record
Abstract
Teacher education worldwide faces challenges in preparing prospective teachers for diverse classrooms. Understanding pre-service teachers’ knowledge and attitudes toward diversity is crucial for achieving this goal. This article details a study surveying first-year pre-service teachers at a Finnish university to evaluate their knowledge of and attitudes toward the Indigenous Sámi people. A web survey (n = 172) was used to collect the data. The data analysis was conducted using Fisher’s exact test and the Pearson chi-squared test. The findings indicate that although the pre-service teachers had some prior knowledge of the Sámi people, there is a need for culturally responsive education (CRE) that meets the Finnish National Board of Education’s standards for teaching about national cultures. The insights derived from this study offer clear directives for fostering inclusive teacher education practices that address the diverse needs of students from Indigenous and multicultural backgrounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".