Bibliographic record
Abstract
Feelings and emotions are an intrinsic part of our everyday life and racialized bodies experience them profoundly and continuously. Through the skin and through the senses, race evokes an affective response. From anger to sadness, fear, and shame for both the dominant culture and the racialized minority, affect has shaped their interactions, dynamics, and relationships. Today, the separation between ‘us’ and ‘the other’ is still palpable within the context of social and political life. By understanding how bodies become racialized, the role of the skin as a visual representation of difference, the duality of melancholia and the concept of disidentification against dominant ideologies, this paper aims to demonstrate that affect and race are constitutive of each other. When looking at racial history through an affective lens, we see that it has been bound by emotions that happen in our daily existence; in instances, moments and encounters that leave a somehow permanent mark. Affect flows and gets stuck, it reveals stories of happiness and stories of trauma. It discloses other ways of knowing and other ways of learning. It helps us look at the past, dwell and learn from it to open new pathways in the lives of marginalized communities. It calls for an urgency to express unconformity toward racial formations and to understand how emotions circulate and move through our own bodies and through the world.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".