Bibliographic record
Abstract
Grief is often seen as a personal response to losing a loved one, but it can also arise from the loss of deeply held values and identities linked to social, structural, and religious spheres. Political grief is a unique form of this, stemming from political policies, laws, and social messaging that certain groups perceive as losses. As societies face political decisions and systemic failures, grief can emerge from losing trust in institutions, shared beliefs, and a sense of belonging. An outgrowth of political grief is a strain on relationships due to polarization, heightened by threat-activating events and resulting tensions. Many people turn to religion to counter feelings of vulnerability and incoherence in today’s political climate. While this may relieve anxiety and provide stability, it can also exacerbate some sources of grief. Understanding these dimensions is crucial for addressing political grief’s broader implications, as individuals and communities seek meaning and attempt to rewrite their narratives in adversity. This discussion includes defining grief beyond death-loss and exploring the interplay between social/political structures and culture. It also considers specific threats and responses, including religious alignment, focusing on recent events in the United States.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".