Theories of Representation in French and English Scholarship on Multilingualism
Bibliographic record
Abstract
Researchers have examined language learning and intercultural contact in multilingual contexts from diverse epistemological and ontological stances. In some cases, the contrasts or incompatibilities in approaches leave little room for joint initiatives and in other cases, the underlying theoretical assumptions converge in several ways, allowing researchers to explore possibilities for interdisciplinary and intercontextual collaboration. In some instances, studies undertaken in different areas share commonalities but they are not explored because of linguistic, disciplinary or geographic barriers. Although only a handful of researchers reference French and English work undertaken in multilingual contexts, there is much to be gained by taking into account what has been written in both languages as it can reveal where there are differences and commonalities in constructs often cited in the literature. We attempt to bridge this gap by highlighting theoretical links that have heretofore been underexplored. We begin by reviewing definitions of social representation and then describe how Francophone sociolinguists drew on this construct to propose the notion of linguistic representation in their studies of multilingual practices. Following this, we discuss related concepts of representation, discourse and identity that have been taken up in English language work.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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 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".