Bibliographic record
Abstract
It is not uncommon to see the terms 'diversity' and 'inclusiveness' used together as a slogan in the context of multiculturalism, inclusive education, diversity management in business, and so on. However, diversity and inclusiveness have opposing vectors: one demands differentiation, while the other demands unification. The aim of this study is to clarify, from an anthropological perspective, the ways in which diversity and inclusiveness are balanced in a multicultural society.\n\nIn this study, I focus on ethnic diversity, cultural diversity, and physical diversity in Vancouver, Canada. On the basis of the ethnographic data collected during my field research in 2012-2013, I analyze how diversity is represented in various places and how people recognize these places by interpreting symbols such as languages, signs and pictograms. Through this analysis, I clarify the range of ways in which diversity and inclusiveness are balanced in different situation. In addition, I describe how people live alongside and with others who have different backgrounds, focusing on their everyday activities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".