Køn og metodevalg blandt samfundsvidenskabelige specialeskrivende
Bibliographic record
Abstract
Feministisk teori og forskning har argumenteret for to sammenhænge mellem køn og forskningsmetoder: Kvinder benytter oftere kvalitative metoder, og køn påvirker valget af forskningsområder. Tidligere forskning baseret på fagfællebedømte publikationer understøtter disse foreslåede sammenhænge, men anerkender bias som følge af homogeniserende mekanismer såsom akademisk professionalisering og fagfællebedømmelse. Vi komplementerer disse studier gennem en analyse af de »nedre lag af akademisk produktion«, specifikt 1.103 socialvidenskabelige specialer, hvilket giver en alternativ vinkel på studiet af køn og forskningsdesign. Vi benytter nylige innovationer indenfor digital tekstanalyse og estimerer en structural topic model for at modellere korpussets latente tematiske struktur. Ud fra denne model tester vi empirisk de foreslåede sammenhænge mellem køn, forskningsmetoder og forskningsområder. Vi finder, at de kvindelige specialestuderende er mere tilbøjelige til at benytte kvalitative metoder, og at nogle forskningsområder er kønnede. Topic modelling bliver demonstreret som et effektivt redskab til at analysere akademiske tekster. ENGELSK ABSTRACT: Rasmus Munksgaard and Oskar Enghoff: Gender and choice of method among social science masters students Feminist theory and research have argued that gender and research methods are related in two ways: women are more likely to employ qualitative methods, and gender affects choice of research area. While previous research on peer-reviewed publications supports these claims, the authors acknowledge that the data is biased due to the homogenizing mechanisms of academic professionalization and peer-review. We complement these previous studies with an analysis of ”lower-level academic production”, specifically 1,103 master’s theses, providing an alternate angle to the study of gender and research design. We employ recent innovations in digital text analysis, and estimate a structural topic model of the corpora to model the latent thematic structure. Using this model, we test the proposed links between gender, research methods and research area. We find that female students are more likely to employ qualitative methods than men, and that some research areas are gendered. Topic modeling is shown to be an efficient tool in the analysis of academic texts. Keywords: Digital methods, academic production, topic modelling, gender.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.011 |
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".