Bibliographic record
Abstract
Bilingual experiences are complex and dynamic (Grosjean, 2013).The complexity reflects the rich language experiences an individual accumulates over a lifetime, and the dynamicity represents how the complexity changes across different language interaction contexts.Essential to understanding the cognitive and linguistic consequences of bilingual experience, the experience of bilingualism ought to be documented.Currently, we know that bilingualism can be captured in multiple dimensions, such as onset age of second language (L2) exposure (see Birdsong, 2018 for a recent review), language dominance (Unsworth, Chondrogianni & Skarabela, 2018), first and second language proficiency (Pérez, Hansen & Bajo, 2019), or the learning context in which L2 acquisition occurs (Montrul, 2019).Researchers in the field use different tools to document language experiences, providing quantity and quality of language acquisition history, daily usage, or home exposure through multiple languages for children and adults.Given the interdisciplinary nature of bilingualism research, if these tools are published at all, they are dispersed across different journals and as appendices of articles.The main purpose of this mini-series is to collect these tools for researchers to choose and adapt.In this mini-series, we have collected five contributions from researchers from the U.S. and Canada who have invested in developing tools for research purposes.These contributions present tools that document bilingual experience from infancy to senior adulthood.We see the mini-series as an opportunity to collectively report tools researchers use to document participants' bilingual experience.These tools will be a systematic collection for students and emerging researchers interested in pursuing research in bilingualism.Recognizing the diverse social contexts where bilingualism occurs is important.Bilingualism, like other experiences, does not happen in a vacuum.The mini-series has included five contributions from research teams in North America.Their tools are designed and grounded in an environment where there is a societal dominant languagenamely, Englishexcept for Byers-Heinlein et al. (Byers-Heinlein, Schott, Gonzalez-Barrero, Brouillard, Dubé, Jardak, Laoun-Rubenstein, Mastroberardino, Morin-Lessard, Pour Iliaei, Salama-Siroishka & Tamay, 2020): her work is situated in Montreal, Canada, where English and French are present in the community.In addition to documenting individual participants' language, describing the societal language use and contact will enrich the interpretation of participant characteristics captured by these tools.Currently, in research adopting a monolingualbilingual comparison, the information about societal language context is not often documented (Surrain & Luk, 2019).Furthermore, new dimensions, such as language entropy and capturing the social interactions of bilinguals, should be considered to supplement the conventional qualifiers of bilingualism (Gullifer & Titone, 2020; Gullifer, Kousaie, Gilbert, Grant, Giroud, Coulter, Klein, Baum, Phillips & Titone, accepted).The first contribution, by Byers-Heinlein et al. ( 2020), has shared a structured interview designed to document infants' language experience, the Multilingual Approach to Parent Language Estimates (MAPLE).In the contribution, Byers-Heinlein et al. (2020) address the key descriptors to document in the interview, as well as interview practices that are engaging, but respectful.Finally, the contribution ends with possible effects that may influence the interpretation of parental reports of children's language environment.Since young children spend a significant amount of time at school, characterizing children's school environment can supplement information captured from home language environment.Castro, Scheffner Hammer, Franco, Cycyk, Scarpino, and Burchinal (2020) share the Center for Early Care and Education Research -Dual Language Learners (CECER-DLL) Child and Family, and Teacher Questionnaires.In this questionnaire, parents and teachers are proxies for capturing young children's home and school language environment.The tool was designed with Spanish-English bilingual families in the U.S. in mind.Castro et al. ( 2020) supplement this tool with a validation study using child assessments as correlates.The Language Experience and Proficiency Questionnaire (LEAP-Q) was first published in 2007 (Marian, Blumenfeld & Kaushanskaya, 2007).Since its publication, LEAP-Q has been used extensively in research involving bilinguals.This tool has been translated to 22 different languages, adapting to different dialects and cultural contexts.This tool reflects the collective effort in the field to improve the necessary linguistic sensitivity when conducting research with
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.117 | 0.069 |
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".