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Record W2951231787 · doi:10.22439/dansoc.v29i1.5741

Køn og metodevalg blandt samfundsvidenskabelige specialeskrivende

2018· article· da· W2951231787 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDansk Sociologi · 2018
Typearticle
Languageda
FieldSocial Sciences
TopicComputational and Text Analysis Methods
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsoskarHumanitiesSociologyProfessionalizationPsychologyPhilosophyArtArt historySocial science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.004

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.

Opus teacher head0.091
GPT teacher head0.436
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it