Culturally Responsive Science Education for Indigenous and Ethnic Minority Students
Bibliographic record
Abstract
The second strand in this chapter focuses on a broader view of student success that encompasses the complex interaction of family, social, cultural, educational, economic, and political contexts. Led in particular by indigenous researchers, this strand contests the characterization of success evident in the fi rst strand, which mainly lies in achievement. Arguments within this view are based on the nature of knowledge and the importance of cultural identity to indigenous and ethnic minority communities. Although little or nothing has changed from the indigenous research point of view forwarded in the earlier chapter (McKinley, 2007), there is still a pervasiveness of unresolved concerns-the devaluation and destruction of people’s cultures, languages, beliefs, and values have all affected and continue to affect indigenous people’s wellbeing. For example, Richards and Scott (2009) argue that a policy of cultural genocide in Canada led to the establishment of residential schools administered by religiousIntroductionSince the writing of the last chapter in the previous handbook (McKinley, 2007), educational research continues to be concerned with indigenous and minority students’ access, participation, and achievement in science education. The underrepresentation of indigenous and some ethnic minority students in secondary science education is a major social and economic disadvantage for these communities and a major challenge for science educators in industrial countries. The reason for this is that the lack of participation and achievement by these communities is perceived as being particularly urgent as they strive for a highly skilled workforce specifi cally in science-based subjects to build their knowledge-based economies. Identifi ed as a “barrier” to this goal is that the number of students being educated in science, technology, engineering, and mathematics (STEM) subjects has fallen (Ezeife, 2003). There are two forces at work here: The numbers of students in science courses from groups who have historically participated has diminished, and the demographics of First World nations’ move toward greater proportions of indigenous and ethnic minority students. In essence, science education research is faced with questions regarding how it can increase the uptake of science-based subjects by students who have previously been excluded from participating in them and, to a large extent, participating in many of their benefi ts.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".