Addressing Community Trauma through the Framework of Controversial Monuments and Monuments of Oppression
Bibliographic record
Abstract
It is well documented that monuments to the confederacy were constructed to invoke terror and fear within Black communities. These monuments were often constructed during periods of time when Black communities made gains to realize full participation in American society, such as Reconstruction (1865–1877) and the Civil Rights movement. For many years, these monuments stood on public grounds such as libraries, courthouses, town squares, and parks. The protests around the world questioned the legitimacy and legacies of these monuments by tearing down not only Confederate statues but also statues of Christopher Columbus and Cecil Rhodes. The presence of these statues invokes a darker period of time and serves as a distinct reminder of the systemic inequalities that we are witnessing today. The presence of the monuments compounds the trauma on affected communities and reinforces indifference in other communities. The Monuments Toolkit was created to empower communities to address contested monuments. Through the lens of recontextualization and reinterpretation, and using case studies of Charleston, South Carolina, and the Indian Residential Schools in Canada, this chapter discusses truth and reconciliation processes and frameworks employed in addressing community traumas and fostering community healing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.050 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".