Plurilingualism as a Catalyst for Creativity in Superdiverse Societies: A Systemic Analysis
Bibliographic record
Abstract
Post-industrial societies are characterized by a high degree of mobility which manifests itself through waves of migration and affects all knowledge domains and all aspects of both individual and collective lives. This situation presents challenges under the pressure of a powerfully uniformizing globalization. However, the exponential increase of diversity linked to intensified mobility is also conducive to social transformations since, when the numerous languages and cultures of the migrants encounter the languages and cultures of the host countries, they act as catalyzers of change. This article considers such social transformation in the light of the concept of plurilingualism as distinct from multilingualism, explaining the advantages of the former over the latter in such contexts, and analyzes possible synergies between plurilingualism and creativity through the lens of complexity theories and the theory of affordances, with the related concepts of 'affordance spaces' and landscape of affordances. After a brief introduction of the main tenets of complexity theories and affordances, the article builds on three complementary models of creativity, using complexity theories as a framework and discusses the specific characteristics and potential of plurilingualism by explaining how it can transform diversity from an obstacle into an opportunity, a possibility for action. The triadic relationship between creativity, plurilingualism, and complexity is considered. As a result, the article suggests that plurilingualism can create conditions conducive to creativity thanks to its multiple and flexible nature that values all forms of cross-fertilization and the uniqueness of the resulting individual trajectories. Without claiming any causal relationship between plurilingualism and creativity, the paper explains the reasons why it is crucial to nurture and foster plurilingualism in order to provide favorable conditions for creativity and change. The article explains the characteristics and implications of plurilanguaging, and the potential for individuals to embrace a holistic, complex view of languages and cultures and to experience empowerment in the process of perceiving and exploring linguistic and cultural diversity, hybridity and interconnections, thus discovering and liberating their full creative repertoire.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".