„Canada meets France“: Recasting identities of Canadianess and Francité through global economic exchanges
Bibliographic record
Abstract
In this chapter I will take up the idea of motion and flow as prominent features of our time and look at their role in creating news forms of language contact and identity through global economic exchanges. My argument relates to studies on economic globalisation (Coe & Yeung 2001), in which the term ‘economic’ is not understood as a form of industrial organisation, but as part of social processes that include language contact and socio-cultural practices (Appadurai 2000). The chapter looks at global flows of goods and people and explores specific moments in which their trajectories cross. The specific focus is on goods and people from Canadian origin who meet in locally distant France where Canadian products are sold on fairs and Christmas markets. This encounter of sells people from French Canada and local French clients entails identity building that is centred around products, local cultures, local language accents and categories of ethnic and national belonging.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.006 | 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 teacher head, 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".