Stigma, Destigmatization and Belonging in Time
Bibliographic record
Abstract
In his book Stigma: Notes from the Management of Spoiled Identity, the American-Canadian sociologist Erving Goffman states that those who bear stigma can never truly shed the discredited characteristics that society attributes to them. Rather, the stigmatized can only hope for a transformation of self “ from someone with a particular blemish into someone with a record of having corrected a particular blemish” (Goffman 1986, 4). Society traps those who bear stigma through discourse in a separate temporal space in which they are always perceived through the lens of their discredited attributes. Stigma, in this way, “fixes” those who bear it in a separate and unequal time. The temporality of stigma also queries the nature of destigmatization, suggesting that those who struggle to rid themselves of stigma’s lingering taint also struggle to exit the temporal fixity that stigma imposes. This project seeks to center temporality and time in this struggle and asks, how might destigmatization look when viewed through a temporal lens? To investigate the temporality of this struggle against the fixity of stigmatizing discourse, this project uses the long struggle waged by African and African-descendant people against the lingering taint of racism, colorism, and colonialism as its lens. By examining the Pan-African discourses articulated at the 1919 Pan-African Congress, the 1945 Pan-African Congress, the 1955 Bandung Conference, and the 1963 inaugural meeting of the Organization of African Unity, this project explores how those who bear stigma articulate diverse and sometimes competing notions about the journey towards destigmatization, which is a struggle over belonging to an equal and dynamic time. This investigation centers on stigmatizing discourses about Black Africanity because they are historically entrenched, globally pervasive, and therefore uniquely suited to this project’s research aim.To chart how the stigmatized act through time, with time, and on time, it draws upon Jacques Rancière’s notion of equality, subjectivity, and the “temporal hierarchy” that divides time between those who have it and those who do not, to understand how Africans and African descendants have imagined a destigmatized time—a future of possibilities, potentialities, and hope.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.079 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".