MétaCan
Menu
Back to cohort
Record W7039829419

Normering av stadnamn: lover, forskrifter og praksis – ein analyse av saker som har nådd Klagenemnda for stadnamnsaker i perioden 2012–2022

2023· other· en· W7039829419 on OpenAlexaboutno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsnot available
Fundersnot available
KeywordsAppealNorwegianSpellingQuarter (Canadian coin)Period (music)Pronunciation
DOInot available

Abstract

fetched live from OpenAlex

The Norwegian Place Name Act (lov om stadnamn) has gone through no less than three major revisions since 2005. The purpose of this study is to examine which sections of the different law texts the Appeal Board for Place Names (Klagenemnda for stadnamnsaker) has empha­sized when they have made decisions about the spelling of Norwegian place names, and which trends can be identified through a study of the actual decisions made by the Appeal Board in the period 2012–2022. The study shows that the main rule in the Norwegian Place Name Act, that one should start from the inherited local pronunciation and follow the spelling principles and rules for Norwegian, has been decisive for a clear majority (67.5%) of the decisions made by the Appeal Board throughout the period. At the same time, the study shows that in around a quarter (23.9%) of the cases, the Appeal Board has made decisions about spellings that deviate from this general rule with reference to various exceptional provisions in laws and regulations. However, there is no statistical basis for claiming that the increasingly liberal legal and regulatory texts have led to an increase in the proportion of decisions on spell­ings that deviate from the main rule throughout the examined period.

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)0.004
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.311
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
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.060
GPT teacher head0.349
Teacher spread0.289 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDuo Research Archive (University of Oslo)Same topicNames, Identity, and Discrimination ResearchFrench-language works237,207