The acquisition of gender: Differences between monolingual Brazilian Portuguese and bilingual Portuguese-English children
Bibliographic record
Abstract
This thesis reports on the results of research investigating the early acquisition of grammatical gender in Brazilian Portuguese (BP) monolingual (L1) and BP- Canadian English bilingual (2L1) children. BP has a two-gender system, with nouns being, grammatically speaking, either masculine or feminine. Canadian English does not present grammatically-gendered nouns. As such, the bilingual (2L1) acquisition of both languages raises the question of whether there will be attrition between the distinct grammatical gender systems. That is, does the acquisition of a grammatically ungendered language such as English Studies influence the acquisition of grammatical gender in the other language (in this case, BP)? Studies in monolingual gender agreement acquisition have already been conducted in Portuguese (Corrêa & Name, 2003; Correa, Augusto & Castro, 2010) and other Romance languages, but they do not account for bilingual acquisition. This is the first study to address the difference between L1 acquisition of BP and 2L1 acquisition where the other concomitant L1 does not present gendered nouns. I compare the rate of acquisition of the grammatical gender system of BP in a L1 context and in a 2L1 BP-CE context. The initial hypothesis of this research is that bilingual Brazilian Portuguese-Canadian English children will demonstrate later grammatical gender acquisition. This will result in later production of correct determiner-noun-adjective gender agreement when compared to monolingual Brazilian children. The results of this study support this hypothesis. Monolingual and bilingual acquisition were compared through elicited production tasks. In these tasks, grammatical gender was attributed to nonce nouns, and children were then asked to produce gender agreement in determiners and adjectives. The tests measured the effects of acquiring a non-gendered language (English) on children’s process of acquiring and producing gender agreement in another language (BP). Bilingual children produced significantly less correct grammatical gender inflections than their similarly-aged monolingual peers. This demonstrates that bilingual children start the grammatical gender acquisition process later than their monolingual counterparts and take a longer time to master the grammatical gender system of BP completely. The data was collected in the province of Saskatchewan, Canada and in the state of Rio de Janeiro, Brazil. The participants were 23 monolingual children and 21 bilingual children, between the ages of 2.4 and 5.2 years old.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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 teacher head, 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".