Culturally Responsive Research Design as Complement to Hegemonic Paradigms in the Comparative, International, Development Educational Context
Bibliographic record
Abstract
Despite profuse research on the matter, the widely acknowledged gap between educational research and teaching/learning practices suggests that considering the cultural cannons of participants under research remains an issue -among others- when addressing the legitimization of knowledge production in Comparative, International, Developing, Educational (CIDE) contexts. Such premise acquires further relevance for initiatives conducted with research participants whose voices are commonly marginalized in the process of designing research instruments, including the youth and children. The present paper aims at analyzing the importance of conducting CIDE research from a culturally responsive approach, and to illustrate that research strategies which bridge the either hegemonic or alternative research dichotomy contribute the legitimacy of knowledge production in contexts including underage subjects. A small-scale pilot research was internationally implemented with two teenage students and two educational researchers from developed and undeveloped contexts to use their epistemologies as input for the design of a data-collection method. Results suggest that omitting the views of participants in the process of research design can risk the legitimacy of knowledge production, and that complementary approaches contribute better the validity of studies conducted in the field of Social Sciences.
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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 | 0.169 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".