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Record W2958762673 · doi:10.1017/s1366728919000312

The English disease in Finnish compound processing: Backward transfer effects in Finnish–English bilinguals

2019· article· en· W2958762673 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

VenueBilingualism Language and Cognition · 2019
Typearticle
Languageen
FieldComputer Science
TopicText Readability and Simplification
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSpellingLinguisticsReading (process)Transfer (computing)Computer scienceLine (geometry)HistoryPsychologyMathematicsParallel computingPhilosophy

Abstract

fetched live from OpenAlex

Abstract Most English compounds are spaced compounds, whereas spelling regulations prescribe Finnish compounds to be written in a concatenated format. However, as in English, Finnish compounds are commonly spaced nowadays (e.g., piha juhla ‘garden party’), a phenomenon that we labeled the ‘English disease’. In this eye movement study with Finnish–English bilinguals we investigate whether the reading of a concatenated or illegally spaced Finnish compound is affected by the spelling of an English translation equivalent (ETE). We found that spaced Finnish compounds were read slower than their concatenated counterparts, but this effect was attenuated when ETEs were thought to be spaced. Similarly, concatenated Finnish compounds were read faster when their ETEs were also concatenated. These backward transfer effects are in line with studies that show that processing behavior in L1 is affected by a strong concurrent L2, even when the L1 is the native language as well as the dominant community language.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.241
Teacher spread0.234 · 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