Bibliographic record
Abstract
Two Theoretical ConceptsIn this chapter, I define the major theoretical concepts that you will see throughout this book.The first concepts are racialization and race, or more specifically, their relationship.A key argument of this book is that diversity discourse is not simply imposed upon "raced" bodies; rather, it recirculates normative and regulatory qualifiers that remake raced bodies and their intelligibility in the City through racialization.Defining the relationship(s) between race and racialization here provides a critical framework from which to understand this argument.In the previous chapter I explained that while some racial Others must be included in order for the City to make itself up as "diverse," their inclusion and, by extension, belonging, is fraught with negotiations.To help explain the complexities of these negotiations and their racial and affective contents, I draw on the notion of abjection.Abjection is also vital to my analyses of diversity discourse in the City of Toronto as being dependent on the invitation to the racial Others to negotiate their belonging, ultimately, to normalize the City and the natural subject who occupies it as white.Next are discourse, power, and space.I define each separately and then as interrelated, so it becomes possible to understand how diversity discourse in the City of Toronto has racial and spatial expressions and implications.Following this, I explain three theorizations of belonging that I draw on in this book: belonging through encounter, belonging through hailing, and belonging as a longing.Applying the first two approaches to belonging, we can begin to understand how various racial subjectivities in the City are hailed by diversity discourse into (re-)affirming particular identifications, values, and meanings, in line with familiar, historical racial norms.However, the stability of these identifications comes into question once we consider the third axis of
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".