Bibliographic record
Abstract
Some musings on multilingualism and the rationale behind this special issueToday multilingualism (re-)appears as a fundamental condition and aim of learning as well as the use of languages in education and in multiple other contexts.Present scholarly contributions rarely link up with historical multilingualism.Investigations into multilingualism in the past are, however, insightful and highly relevant.Let us take Mary Louise Pratt's prominent discussion of the Guaman Poma's New Chronicle and Good Government from 1613 (Pratt, 1991) or Rindler Schjerve's volume on language policy in the 19th century Habsburg Empire as an example (Rindler Schjerve, 2003).Among many other insights, these works remind us that multilingualism is neither a recent phenomenon, nor a characteristic restricted to modern globalized societies.Multilingualism, in its widest sense, is and has always been a fundamental feature of human life in society.We would like to point out two implications of this observation: First, the study of language cannot be separated from the conditions of human life.The opposite also holds true: social research cannot ignore language.Second, the insights from historical multilingualism re-confirm that research in multilingualism brings out big issues of humanity such as power, equity, or identity.This comes to the forefront even more when the context is education, since the distribution of chances for a good (or better) life and access to resources is closely related to education.Hence, it comes as no surprise that research into multilingualism and education is particularly prolific.There is an impressive number of recent publications that stimulate the field and inspire new questions (e.g.Man Chu Lau & Van Viegen, 2020;Tian et al., 2020 or Sànchez & García, 2021 in the field of translanguaging) or reaffirm decades of previous research (e.g.Cummins, 2021).Even these few examples show an important fact about the state of multilingualism research: knowledge about the complexities of multilingual learning and teaching has increased tremendously.Moreover, research has provided
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.212 | 0.085 |
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".