Bibliographic record
Abstract
The purpose of this essay is to investigate religious didactic research that could eventually be applied in a teacher’s various forms of teaching and education, so that islamophobia may be eradicated, which leads me to this essay’s question How can religious didactic research be applied in religious education to combat Islamophobia in schools and classrooms?. The research required for this essay has been found through various databases, such as Google Scholar, Swepub and ERIC via EBSCO. Through the selected research in this essay, I found that the research had intentions to combat questions and doubts regarding islamophobia in schools, which made it easy for me to choose them. Even though most of my research is established in several foreign countries such as Canada and the United States, I find my research and their methods relevant to Swedish society as well. I believe my research is more than helpful for me as a yet to be teacher, but also for all the teachers out there. The research combined is able to give the tools needed for a teacher to at least reduce islamophobia in classrooms. Examples of how to do that will be presented in this essay. It is not unknown that prejudice and mockery are things that people have experienced throughout time and therefore I find that this subject is of huge relevance.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.059 |
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".