Collecting language acquisition data from understudied urban communities: A reply to Cristia et al.
Bibliographic record
Abstract
In the target article, Cristia, Foushee, Aravena-Bravo, Cychosz, Scaff, and Casillas (2022) convincingly show the need to broaden the current language acquisition research base, not only in linguistic diversity, but also in terms of regions and cultural groups studied. In conducting acquisition research in understudied populations, such as in rural settings, the authors highlight the importance of using a multi-method approach. They present the challenges in adapting these methods to new settings and offer possible ways to promote this type of research. In this commentary, we extend the discussion to understudied urban communities, as we encounter several of the concerns raised in Cristia et al. when collecting observational and experimental language acquisition data from Metro Manila, Philippines. We first describe the community we study, the challenges and modifications needed for conducting research in this setting, and end with a discussion of possible strategies to promote research in communities with understudied populations.
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.
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 | 0.052 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.044 | 0.082 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".