Innledning: Å forske med institusjonell etnografi
Bibliographic record
Abstract
Institusjonell etnografi er en forskningstilnærming som opprinnelig ble utviklet av den kanadiske sosiologen Dorothy E. Smith, og som har som formål å utforske og utfordre maktforhold i samfunnet. I Norge og Norden for øvrig brukes tilnærmingen av forskere fra ulike disipliner og fagfelt. I Erfaringer med institusjonell etnografi får vi innblikk i ulike forskeres motivasjon for, erfaringer med og refleksjoner rundt å sette seg inn i og bruke institusjonell etnografi i sin egen forskning. Vi får innblikk i bestemte forskningsprosesser og metodiske grep i slike prosesser, i erfaringer med å bruke sentrale begreper fra institusjonell etnografi og i refleksjoner over forskerrollen. Boken er et nyttig redskap for forskere, masterstudenter og stipendiater som benytter – eller vurderer å benytte – institusjonell etnografi i sin forskning. Synopsis in English: Originally developed by the Canadian sociologist Dorothy E. Smith, institutional ethnography is an approach to research that aims to explore and challenge power relations in society. In Norway and the other Nordic countries, the approach is used by researchers in a variety disciplines and professional fields. In Experiences with Institutional Ethnography, we gain insight into several different researchers’ motivation for, experiences with and reflections on delving into and using institutional ethnography in their own research. We gain insight into specific research processes and their attendant methodological approaches, into experiences of using key concepts from institutional ethnography, and into reflections on the researcher's role. This book will be a useful tool for researchers, master’s students and research fellows who utilize – or are considering utilizing – institutional ethnography in their work.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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.
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".