Bibliographic record
Abstract
Affective cycles concept, 97 Affective experiences of gaze in everyday life, 98-114 visibility, authenticity and bodies in/ out of place, 103-114 Affective politics of mixed race, 2, 117-119, 125 Ahmed's approach to phenomenology, 96 Anti-Black racism, 7, 9-10, 50, 57-58, 120 Anti-Indigenous racism, 54-55 Anticipation, 118 Australia, 4-5, 106-107, 119 Australian context, 4-5 Belonging re-negotiating terms of, 80-91 terms of engagement, 66-91 Biographization process, 19 Biracial, 70 Black Atlantic, 7 Black masculinity, 52-53 Black/Métis mix, 125 Black/white mix, 125 Blackness, 50-51 British colonial entanglements, 3-6 British/Commonwealth settler colonial states, 4-5 Brownness, 62, 123-124 Calgary, 2, 13-16, 33-34 Canada analyzing media discourses on mixed race, 30 mixed race discourses in newspaper media, 34-39 multicultural era and race discourse, 25-39 race and mixed race in, 9-14 race-multicultural discourse in Canada, 30-33 understanding racial gaze in multicultural Canada, 27-30 and white settler colonial states, 3-6 Canadian census reporting, 10-13 Canadian context, 8, 27-29 and (un)collective possibilities, 119-125 global mixed race literatures and, 3-9 Canadian culture, 56 Canadian identity, 74 Canadian institutions, 9-10 Canadian media, 35 Canadian multicultural discourse, 2, 27-28 Canadian multiculturalism, 27-28 Canadian Multiculturalism Act (1988), 26 Canadian official multiculturalism, 119 Canadian population, 12 Canadian settler colonial state, The, 5 Celebratory discourse of racial mixing, 29-30 Celebratory Multicultural Nation, 30, 32, 77-78 Census, 5-6, 10, 12, 66, 75-76 Childhood school experiences, 103-104 Children, 1, 37-38 Chinese community, 48 Collectives, 122-124 Communities of brownness, 123 Complex commonalities, 80, 85, 91 Critical Mixed Race Studies (CMRS),
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.771 | 0.526 |
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".