Bibliographic record
Abstract
While official rhetoric of multiculturalism claims to value cultural diversity, everyday multiculturalism focuses on how people of diverse cultural backgrounds live together in their everyday lives. Research on everyday multiculturalism has documented ways through which people negotiate senses, sensibilities, emotionality, and relationality across intercultural contact zones. While recognizing the importance of human intentionality and community in conditioning coexistence, this article also points to the constitutive power of practice-based learning that emerges through the coming together of human and nonhuman beings. Drawing on a qualitative study of the learning experiences of six Chinese immigrants in community gardens on a university campus in Canada, this article shows three ways of learning that foster knowing, connecting, and hybrid knowledge production across cultures: (a) learning through communities of conviviality; (b) learning mediated through nonhuman things such as land, waste, and free-floating seeds; and (c) learning through assemblages that fold in culture, place, and space.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.031 |
| Research integrity | 0.002 | 0.005 |
| 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 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".