Geiriau Saesneg yn slipio i fewn Investigating the

“Geiriau Saesneg yn slipio i fewn”: Investigating the integration of English -origin verbs in Welsh Jonathan Stammers 8 March 2010, Bilingualism Centre

Overview The Siarad corpus Code-switching vs. borrowing controversy Poplack approach: “Nonce Borrowing” English-origin verbs in Welsh Analysis: Soft mutation on verbs (2 attempts) Dealing with word frequency effects Summary

The Siarad Corpus 40 hours of Welsh/English bilingual speech recorded & fully transcribed in CHAT format 69 Naturalistic recordings of informal conversations, typically between 2 speakers, & 30 minutes long; 151 speakers of varying age, sex and background 456, 266 words (tokens) Every word tagged for language Recordings & transcription done by project team (Elen Robert, Peredur Davies, Marika Fusser & myself; Margaret Deuchar – project director) Freely available to researchers online

Examples in Siarad: Borrowings? ond mae o mor cheesy mae’n funny yndy ? “but it’s so cheesy it’s funny isn’t it? ” [Fusser 29: 217] hynna ’dy’r exam dw i gorod eistedd fory “that’s the exam I have to sit tomorrow. ” [Stammers 6: 1273]

Code-switching or Borrowing? Criteria: Criterion Borrowing no more than one word adaptation: phonological morphological syntactic frequent use replaces own word recognised as own word semantic change Code-mixing + ±/- + + + - (Muysken 2000: 73) Additional Criteria suggested: “Core/Cultural” distinction: “Cultural” items are not switches Flagging: self-correction, repetition, hesitation or stammering flags up a switch Dictionary

Poplack’s approach Code-switching and borrowing can be distinguished absolutely “Free morpheme constraint” no wordinternal switching Variationist approach: Comparing morphosyntactic patterning of donor-language items with native items “Nonce Borrowing hypothesis”

The Nonce borrowing hypothesis “One of the goals of these studies is to develop operational criteria for distinguishing loanwords from codeswitches. Thus, for the Puerto Rican data, a working hypothesis was that loanwords from English were phonologicaly, morphologically, and syntactically integrated into Spanish, were recurrent and widespread, and that an English word not satisfying these criteria could only occur in English monolingual discourse or in code-switches from Spanish to English. In general, however, borrowing is a much more productive process and is not bound by all of these constraints. In particular, phonological integration and the “social” characteristics of recurrence (in the speech of an individual) and distribution (across the community) need not be satisfied. This type of borrowing is sometimes called “nonce” borrowing. ” (Sankoff, Poplack & Vanniarajan 1990: 74)

Study Language pair studied Elements analysed Linguistic features studied in analyses Conclusion Sankoff, Poplack & Vanniarajan 1990 Tamil-English Lone English nouns Case inflections All are borrowings Poplack & Meechan 1995 Wolof-French; Fongbe-French Lone French nouns Definite/indefinite reference ; NP word order All are borrowings Adalar & Tagliamonte 1998 Turkish-English Lone English nouns Vowel harmony; Plural affixation; NP word order All are borrowings Budzhak-Jones 1998 Ukrainian-English Lone English Case inflections All are borrowings Eze 1998 Igbo-English nouns Samar & Meechan Persian-English 1998 Lone English verbs; Lone English nouns Affix distribution; serial All are constructions; vowel harmony (verbs) borrowings ; determiners; type of nominal reference; NP word order (nouns) Lone English nouns Definite/indefinite reference; VP word order; case inflections All are borrowings Determiners; NP word order; plural marking; discourse flagging Most are borrowings; Minority are switches Turpin 1998 (Acadian) French- Lone English nouns English Arroyo & Tricker 2000 Catalan-Spanish Lone Spanish nouns Definite/indefinite reference; plural marking; gender All are borrowings Shin 2002 Korean-English Lone English nouns Case inflections All are borrowings Cacoullos & Aaron 2003 Spanish-English Lone English nouns Determiners All are borrowings

English verb insertions (1) More “established English borrowings”: pasio (to pass), trio (to try), setlo (to settle), canslo (to cancel), meindio (to mind), cysidro (to consider) sut mae o’n cope-io efo (. ) hynna i gyd? “how is he coping with all that? ” [Fusser 29: 635] pan dach chi’n defnyddio wide-angle lenses dach chi’n emphasize-io ’r foreground. “when you use wide-angle lenses, you emphasize the foreground. ” [Fusser 17: 792]

English verb insertions (2) bysai hi’m ’di gwisgo helmet ’sai pen hi ’di cael ei crush-o to bits “if she hadn’t worn a helmet, her head would have been crushed to bits. ” [Robert 3: 898] a mae ’di cael ei ºgonnect-io i’r printer yr computer, de “and it’s been connected to the computer printer, right. ” [Roberts 2: 627]

English verb insertions (3) anyway, ges i ’yn gazump-io ar hwnna “anyway, I got gazumped on that one” [Fusser 29: 700] maen nhw’n (. ) exfoliate-io chdi gynta (. ) ac yn spwnjo chi drosodd gynta “they exfoliate you first, and sponge you over first” [Fusser 30: 27]

Soft Mutation in Welsh

Soft mutation on verbs: Environments (1) After "i" particle e. g. oedd e’n mynd i ºgostio pres After "ei" possessive (with masculine subject) e. g. fyswn [Fusser 6: 524] i licio ei ºfenthyg o [Fusser 9: 375] After various other particles: heb, am, cyn, gan, ar, neu; dy possessive e. g. sut mae o am ºfihafio [Fusser 15: 510]

Soft mutation on verbs: Environments (2) With gwneud (or ddaru) auxiliary + Subject e. g. wnest ti ºdrio? [Stammers 5: 708] After "i" + (non-overt) Subject e. g. mae’n gwneud i chdi ºgofio rywbeth dydy? [Stammers 7: 139] After Finite Verb + Subject e. g. sut fedra i ºddeud? [Fusser 4: 257]

Soft Mutation: Variation E. g. Welsh verb “cerdded” (to walk): a maen nhw’n mynd i ºgerdded am tua dwy, dair milltir “and they’re going to walk for 2 or 3 miles” [Roberts 2: 32] But frequently mutation doesn’t happen where expected (especially in informal spoken Welsh): a (. ) does dim byd i poeni amdano “and there’s nothing to worry about” [Fusser 14: 40]

Three groups of verbs compared in st this study (1 Analysis): Native Welsh: cofio (remember), defnyddio (use), cwyno (complain), pwyso (push), cneifio (shear), treiglo (mutate), twtio (tidy) talu (pay), penderfynu (decide), poeni (worry), lladd (kill), cwrdd (meet), cau (close), dal (hold), dechrau (start), cael (have), mynd (go), gweld (see). [irregular verbs and non –(i)o suffix included] Listed English: trio, cario, clirio, dreifio, sbwnjo, clariffeio, pinsio, bargeinio, pipo, dipio, trotio, manejio, tsiecio, titso, protestio, cidnapio twtsiad, dripian, [non –(i)o suffix included] Unlisted English: text-io, download-io, brief-io, quote-io, bulkio, ban-io, bypass-io, crush-o, trample-o, base-io, connect-io, babysit-io, decorate-io, concentrate-io, mollycoddle-io, powerwalk-io

Method Text-based searches through corpus (and using word frequency lists) for possible verbs, extracting examples where mutation expected (and where consonant can be mutated!) Coded each verb as mutated or not First attempt: used a random sampling technique to find the native Welsh verbs

Results (First Attempt): (1)

Results (First Attempt): (1) Absolute Freq. /million words %Mut. Overall AVG Freq 1 -4 1 -9 34. 69% 2. 21 0. 3452 5 -45 10 -99 52. 68% 16. 44 1. 2160 46 -450 100 -999 75. 29% 161. 56 2. 2083 451 -4500 1000 -9999 89. 63% 1962. 67 3. 2928 0. 7752 0. 9936 Correlation coefficient with overall % mutation: log(AVG freq)

Analysis: 1 st & 2 nd Attempts Earlier Analysis Later Analysis Corpus 46 transcript subset (66%) of Siarad Whole Siarad corpus; 301, 072 word tokens (69 transcripts; 456, 266 word tokens) Instances selected (where soft mutation expected) All English-origin verbs (with any suffix or none); Sample of native tokens: 5 randomly distributed tokens per transcript of any Welsh verbs, including irregular verbs 466 tokens altogether (230 native Welsh; 198 listed English; 38 unlisted English) All English-origin and native tokens ending in the –(i)o suffix (regular verbs only) No. of verb types (overall and by verb status and frequency band) 147 types overall 159 types overall native Welsh: 65; listed English: 62; unlisted English: 20 native Welsh: 44; listed English: 81; unlisted English: 34 1 -9 words per million: 42; 10 -99: 54; 100 -999: 41; 1000 -9999: 10 1 -9 words per million: 79; 10 -99: 72; 100 -999: 7; 1000 -9999: 1 Initial consonants Verbs starting with /p/, /t/, /k/, /b/, /d/, /m/, /ɬ/ and /g/ included; /rʰ/ excluded Verbs starting with /p/, /t/, /k/, /b/, /d/ and /m/ included; 506 tokens altogether (143 native Welsh; 302 listed English; 61 unlisted English) /ɬ/, /g/ and /rʰ/ excluded

Other Possible Variables: (1) Mutation Environment (A) "i" particle (B) "gwneud" auxiliary + Subject (C) "i" + (non-overt) Subject (D) "ei" possessive (E) Fin Verb + Subject (F) other particle

Other Possible Variables: (2) Initial Consonant

Three groups of verbs compared in nd this study (2 Analysis): Native Welsh: cofio (remember), defnyddio (use), cwyno (complain), pwyso (push), cneifio (shear), treiglo (mutate), twtio (tidy) Listed English: trio, cario, clirio, dreifio, sbwnjo, clariffeio, pinsio, bargeinio, pipo, dipio, trotio, manejio, tsiecio, titso, protestio, cidnapio Unlisted English: text-io, download-io, brief-io, quote-io, bulk-io, ban-io, bypass-io, crush-o, trample-o, base-io, connect-io, babysit-io, decorate-io, concentrate-io, mollycoddle-io, power-walk-io

Results: Second Analysis

Results: First & Second Analyses

Results: 1 st & 2 nd Attempts Earlier Analysis Later Analysis % Mutation by verb status native Welsh 85. 6%; listed English 61. 1%; unlisted English 18. 4% native Welsh 72. 7%; listed English 66. 2%; unlisted English 16. 4% % Mutation by frequency band 1 -9 words per million 34. 7%; 1 -9 words per million 40. 9%; 10 -99 52. 7%; 10 -99 58. 9%; 100 -999 75. 3%; 100 -999 74. 9%; 1000 -9999 89. 6% 1000 -9999 86. 7%

Results (First Analysis)

Results (Second Analysis)

Statistical Testing: 1 st & 2 nd Analyses Earlier Analysis Later Analysis Results of statistical testing (logistic regression) with raw frequency values Raw frequency marginally significant (p=. 044) or not quite significant (p=. 072) as a predictor of mutation, depending upon baseline category Raw frequency marginally significant (p=. 042) or not at all significant (p=. 682) as a predictor of mutation, depending upon baseline category Differences between all verb categories significant, including between native Welsh and listed English (where p<. 0005) Differences between verb categories significant, except between native Welsh and listed English (where p=. 174) Results of statistical testing (logistic regression) with log values of frequency Log frequency significant (p=. 019 or. 01 with native and listed English as baseline respectively) as a predictor of mutation Log frequency highly significant as a predictor of mutation (p=. 001) with listed English as baseline, but not at all significant (p=. 549) with unlisted English as baseline) Differences between native Welsh and unlisted English, and between listed and unlisted English significant (p=. 019 and. 03, respectively), but difference between native Welsh and listed English not at all significant (p=. 448) Differences between native Welsh and unlisted English, and between listed and unlisted English highly significant (p=. 001 and. 005, respectively), but difference between native Welsh and listed English not at all significant (p=. 186)

Summary English-origin verbs in Welsh – highly productive (―(i)o) suffix). Almost certainly be considered a simple case of borrowings according to Poplack Subset of them based on a dictionary criterion found to be significantly less integrated morpho-syntactically (with respect to soft mutation) : could be considered “switches” Strong (log-linear) relationship between word frequency and rate of mutation This goes against Poplack’s “nonce borrowing” hypothesis: “nonce” items pattern significantly differently from “established” items, based on either dictionary criterion OR frequency