Mediation practices of pluri-lingual/cultural, and transnational educators and researchers: a collaborative autoethnography
Bibliographic record
Abstract
In shifting the field of applied linguistics towards more dynamic and holistic language perspectives and practices, the concept of mediation has helped frame language use and users as part of social, agentive, collaborative communication processes within and across languages. While mediation is typically used in relation to language learners (Council of Europe, Citation2020), the concept can also be applied to language professionals. This collaborative autoethnographic study explores the mediation practices influencing the identities, teaching, and research of two pluri-lingual/cultural, transnational educators. Adopting a collaborative autoethnographic approach (Chang et al., Citation2013), we collected data through interview questions, tandem writing and written dialogic conversations. The collected data was analyzed through an inductive thematic approach (Braun et al., Citation2019) to capture the plurilingual, pluricultural, and transnational repertoire and mediation practices we engage in. We position ourselves as participants to co-explore our distinct and intersecting lived experiences as teachers and researchers in applied linguistics and language education. Our study suggests that mediation transcends the confines of a primarily pedagogical application, revealing to be a dynamic and fundamentally constitutive process for the negotiation of identities as individuals, educators, and researchers, as it actively molds the modalities individuals adopt to create new meaning.
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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".