Exploring culture, language and the perception of the nature of science
Bibliographic record
Abstract
One dimension of early Canadian education is the attempt of the government to use the education system as an assimilative tool to integrate the First Nations and Me´tis people into Euro-Canadian society. Despite these attempts, many First Nations and Me´tis people retained their culture and their indigenous language. Few science educators have examined First Nations and Western scientific worldviews and the impact they may have on science learning. This study explored the views some First Nations (Cree) and Euro-Canadian Grade-7-level students in Manitoba had about the nature of science. Both qualitative (open-ended questions and interviews) and quantitative (a Likert-scale questionnaire) instruments were used to explore student views. A central hypothesis to this research programme is the possibility that the different world-views of two student populations, Cree and Euro-Canadian, are likely to influence their perceptions of science. This preliminary study explored a range of methodologies to probe the perceptions of the nature of science in these two student populations. It was found that the two cultural groups differed significantly between some of the tenets in a Nature of Scientific Knowledge Scale (NSKS). Cree students significantly differed from Euro-Canadian students on the developmental, testable and unified tenets of the nature of scientific knowledge scale. No significant differences were found in NSKS scores between language groups (Cree students who speak English in the home and those who speak English and Cree or Cree only). The differences found between language groups were primarily in the open-ended questions where preformulated responses were absent. Interviews about critical incidents provided more detailed accounts of the Cree students' perception of the nature of science. The implications of the findings of this study are discussed in relation to the challenges related to research methodology, further areas for investigation, science teaching in First Nations communities and science curriculum development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".