Towards Diversifying Early Language Development Research: The First Truly Global International Summer/Winter School on Language Acquisition (/L+/) 2021
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With a long-term aim of empowering researchers everywhere to contribute to work on language development, we organized the First Truly Global /L+/ International Summer/ Winter School on Language Acquisition, a free 5-day virtual school for early career researchers. In this paper, we describe the school, our experience organizing it, and lessons learned. The school had a diverse organizer team, composed of 26 researchers (17 from under represented areas: Subsaharan Africa, South and Southeast Asia, and Central and South America); and a diverse volunteer team, with a total of 95 volunteers from 35 different countries, nearly half from under represented areas. This helped world-wide Page 5 of 5 promotion of the school, leading to 958 registrations from 88 different countries, with 300 registrants (based in 63 countries, 80% from under represented areas) selected to participate in the synchronous aspects of the event. The school employed asynchronous (pre-recorded lectures, which were close-captioned) and synchronous elements (e.g., discussions to place the recorded lectures into participants' context; networking events) across three time zones. A post-school questionnaire revealed that 99% of participants enjoyed taking part in the school. Not with standing these positive quantitative outcomes, qualitative comments suggested we fell short in several areas, including the geographic diversity among lecturers and greater customization of contents to the participants’ contexts. Although much remains to be done to promote inclusivity in linguistic research, we hope our school will contribute to empowering researchers to investigate and publish on language acquisition in their home languages, to eventually result in more representative theories and empirical generalizations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it