What are essential elements of valid research: The problem of 'data' and their collection in cross-cultural contexts
Bibliographic record
Abstract
Professor Judith Bernhard teaches in the School of Early Childhood Education at Ryerson University. She draws on her own experience of research in the field of transnational families to illustrate the challenges of collecting data that can inform practice in the field. She begins by outlining her own personal history as an immigrant and an academic. The difficulties of attempting to be both an advocate for families and a scholarly investigator touch her personally. She uses two cases to illustrate some of these difficulties. The first is the case of divergent perspectives on the role of a parent in the case of a young adolescent whose family had come to Canada from El Salvador. The girl’s teacher and her family differed in their views on her rights and responsibilities, based on their cultural values. Dr. Bernhard describes how these different views were also represented among the members of the research team. Her own roots in South America gave her an understanding of the family’s position, while her academic training allowed her to sympathize with the teacher’s purposes. The team wrote a report that did not fully reflect the tension between the cultures; however, Dr. Bernhard concludes that researchers need to be more mindful of their own professional training and acculturation as important markers to consider in the research process. The second case she presents raises the difficulties she encountered using interviews to investigate the lived experience of refugee Somali mothers. The first interviewer, although an experienced researcher with immigrant roots, proved unacceptable to the Somali families, mostly because he was male. The next person chosen came from the Somali community and was trained by the researchers to obtain informed consent and conduct standard interviews. Dr. Bernhard describes how cultural perceptions and norms made it impossible to use these standardized approaches with the population under investigation, meaning that no credible research paper on the findings could be published. This in turn meant that information about the families’ situation and views would not reach professionals and policy makers whose decisions influence the families’ lives. Dr. Bernhard encourages the research community to reflect on how to take cultural issues into consideration when deciding what is meant by significant “data” and the best methods for collecting them.
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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 | 0.875 | 0.925 |
| Meta-epidemiology (narrow) | 0.004 | 0.010 |
| Meta-epidemiology (broad) | 0.017 | 0.006 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.033 | 0.191 |
| Scholarly communication | 0.059 | 0.077 |
| Open science | 0.017 | 0.030 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".