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Writing Standard: Process of Macedonian Language Standardization

2008· article· en· W2020798792 on OpenAlexvenueno aff
Christina E. Kramer

Bibliographic record

VenueCanadian Slavonic Papers · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Language and Interpretation
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationMacedonianNorm (philosophy)LinguisticsStandard languageProcess (computing)Computer sciencePolitical scienceLawProgramming language

Abstract

fetched live from OpenAlex

This paper focuses on questions of Macedonian standardization at the most micro-level, i.e., within the individual. Through examination of archival materials of Macedonian writers of the early twentieth century, questions of language shift and standardization are addressed. While much research has been conducted on the state processes of language standardizing, on access to the media in newly standardized linguistic codes, and on access to education, this work refocuses discussion of language standards on individual speakers and writers: how and why they shift their language to the emerging norm. Two writers from this period, Anton Kavaev and Radoslav Petkovski, serve as models and provide the first step in a larger study of processes of standardization in the early decades of the twentieth century leading to codification in mid-century. The written works of the authors under study demonstrate that language codification is not an act, nor a series of acts, but a process, a process that takes place within individual speakers who are committed to the project of language standardization while subject to external political and linguistic pressures.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0080.019
Scholarly communication0.0090.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.283
Teacher spread0.276 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations3
Published2008
Admission routes1
Has abstractyes

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