Bibliographic record
Abstract
Koodinvaihdolla tarkoitetaan kielen tai kielimuodon vaihtumista toiseksi samassa puhetilanteessa tai tekstissä. Koodinvaihtoja on tutkittu paljon, mutta niiden prosodisten piirteiden käsittely on jäänyt lähinnä maininnan tasolle. Tämä artikkeli tarkastelee lähemmin koodinvaihtojen prosodiaa. Tavoitteena on koodinvaihtoja sisältävää keskusteluaineistoa analysoimalla valottaa prosodian osuutta koodinvaihtojen merkitysten tulkinnassa. Tutkimuksen aineistona on puolen tunnin puhelinkeskustelu, jossa Suomessa syntynyt mutta pitkään Kanadassa asunut nainen keskustelee Suomessa asuvan siskonpoikansa kanssa. Aineistossa on yhteensä 73 koodinvaihtoesiintymää, joissa keskustelun kieli vaihtuu hetkellisesti suomesta englanniksi. Menetelmällisesti tutkimus nojautuu keskustelunanalyysiin. Tutkimuksen tulokset osoittavat, että useimmiten koodinvaihtoihin liittyy sävelkorkeuden ja intensiteetin nousu. Tyypillisiä koodinvaihtotapauksia, joissa sävelkorkeus ja intensiteetti nousevat, ovat lainasanat ja referointi. Lainasanoissa nousulla ohjataan vastaanottajan huomiota ja haetaan hänen reaktiotaan sanoihin, jotka ovat olennaisia vuoron sisällön tai kertomuksen kannalta mutta joiden tunnistamisessa saattaisi olla ongelmia. Referoinnissa sen sijaan prosodiset muutokset auttavat luomaan ”toisen äänen”. Tällöin sävelkorkeuden ja intensiteetin nousemiseen voi liittyä myös värittynyt äänenlaatu. Molemmissa tapauksissa prosodinen kohosteisuus koodinvaihdossa kutsuu vastaanottajaa terästämään huomiotaan kyseisiin sanoihin tai ilmauksiin. Nousevan sävelkorkeuden lisäksi aineistossa esiintyy myös jonkin verran ympäristöä matalammalta sävelkorkeudelta ja hiljaisemmalla äänellä lausuttuja koodinvaihtoilmauksia. Tällöin koodinvaihdot ovat pikemmin vuorovaikutusta jäsentäviä ilmauksia kuin kertomuksen kannalta olennaisia sisältösanoja ja enemmän puhujalle itselleen kuin vastaanottajalle suunnattua puhetta. Prosodian vaihtelulla voidaan siis säädellä vastaanottajuuden astetta ja merkitä käytössä olevien kielten suhdetta toisiinsa. Code-switching and prosody: A case study of the speech of an expatriate Finn living in Canada Code-switching occurs when a speaker alternates between two or more languages or language varieties in the same conversation or text. As a phenomenon, code-switching has been investigated fairly extensively, but the prosodic features that characterise many of the switches have not gained much attention in previous studies. This article examines the prosody of code-switching instances in more detail. By analysing naturally occurring conversational data, the article sheds light on the role that prosody plays in the interpretation of code-switching instances. The data consists of a 30-minute telephone conversation in which a lady, who was born in Finland but has lived in Canada for a long time, talks with her nephew, who lives in Finland. The conversation features 73 occurrences of code-switching in which the language of the conversation changes momentarily from Finnish to English. The method of our study is that of Conversation Analysis. The results of the study show that the instances of code-switching are usually produced with raised levels of pitch and intensity. Speakers typically use higher pitch and intensity when uttering loanwords or reported speech. As for loanwords, such prosodic marking invites the recipient to pay attention to the word that carries the rise in order to elicit a reaction to words whose recognition might be problematic. In the case of reported speech, however, prosodic features help to create a “second voice”. In addition to changes of pitch and intensity, the quality of voice can be marked in reported speech. In both these cases the prosodic marking carried by the code-switching invites the recipient to pay attention to the words or expressions in question. In addition to raised pitch and intensity, the data also includes several instances in which the code-switching is marked with lowered levels of pitch and intensity. In these cases, the code-switching typically consists of expressions that structure the interaction rather than constitute important content words as regards the ongoing narration. Furthermore, these instances are also more directed towards the speaker herself than to the recipient. Thus, prosodic changes can be used to regulate the degree of recipiency and to indicate the relationship between the languages being used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".