Living a Curriculum of Hyph-E-Nations: Diversity, Equity, and Social Media
Bibliographic record
Abstract
This study considers the complexities of living a cross-cultural curriculum within the multicultural contexts of Canada through following the experience of some first generation immigrants in a project that employs the multi-dimensional space of the Internet and cyber social communities within a vocational public school in Ontario. Disrupting traditional conceptions of students’ production of literacies, the project seeks to rework the boundaries that define multiculturalism as a series of homogeneous hyphenated spaces from which students who are racialized as non-white are expected to speak. Here we consider, “what is at play in the hyphen?” and “how might the networked classroom space be considered a hyph-e-nation?” To explore these questions, we begin with an overview of multicultural education in Canada. We then employ a reading of Third Spaces and quantum physics to reread how students might open up dual Third Spaces through self representations in a social networking space: first through the social network as a Third Space and second, as certain kinds of learners caught in the hyph-e-nated middle of Canadian multiculturalism in an Ontario classroom. The case studies are followed by a discussion that problematizes discourses of comparison between cultural communities of which students with many cultural backgrounds and experiences are members.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".