Bibliographic record
Abstract
Forskningen i mediediskurser i Danmark viser, at etniske minoritetsmænd med muslimsk baggrund fremstilles ud fra negative kønsstereotyper som uciviliserede, voldelige og aggressive. På baggrund af denne forskning analyserer denne artikel, hvordan etniske minoritetsmænd med muslimsk baggrund oplever, at deres maskulinitet bliver positioneret i Danmark. Endvidere undersøges og analyseres, hvilke strategier etniske minoritetsmænd med muslimsk baggrund anvender for at håndtere den oplevede positionering. Undersøgelsen er baseret på 15 interviews med etniske minoritetsmænd med muslimsk baggrund, som alle enten er født eller opvokset i Danmark. Artiklens resultater viser, at samtlige interviewede mænd oplever, at de positioneres ud fra negative stereotyper på baggrund af deres maskulinitet i krydsfeltet mellem religion, etnicitet og alder. Yderligere oplever de på baggrund af oplevede positioneringer, at deres maskulinitet er marginaliseret i det danske samfund. Analysen viser også, at mændene anvender specifikke strategier til at håndtere de oplevede positioneringer. Artiklens teoretiske ramme udgøres af teorier om maskulinitet, intersektionalitet, othering, agency og social identitet.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".