MétaCan
Menu
Back to cohort
Record W4388111183 · doi:10.23865/noasp.195.ch0

Innledning: Å forske med institusjonell etnografi

2023· book-chapter· no· W4388111183 on OpenAlexaboutno aff
May-Linda Magnussen, Ann Christin E. Nilsen

Bibliographic record

Venuenot available
Typebook-chapter
Languageno
FieldPsychology
TopicEducation, Healthcare and Sociology Research
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-Champaign
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.819
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0430.056

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 head0.302
GPT teacher head0.485
Teacher spread0.183 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicEducation, Healthcare and Sociology ResearchFrench-language works237,207