Bibliographic record
Abstract
Grief is usually understood as the personal response to loss. Thus, there is a tendency to consider grief as an individual experience, most typically related to the death of a loved one. However, recent research and theory have provided a much more complex picture of grief as a broad, interdimensional experience that can be both generated and experienced at micro, mezzo, and macro levels. In this context, consideration is given to grief that occurs as a result of events that take place at the sociopolitical level, which can be experienced both individually and collectively. Collective grief may occur when the loss relates to a group where commonly shared assumptions are shattered. The concept of political grief can be seen as a poignant sense of assault to the assumptive world of those who struggle with the ideology and practices of their governing bodies and those who hold political power. Likewise, political grief would also include the direct losses that are experienced by individuals as a result of political policies, ideologies, and oppression enacted and/or empowered at the sociopolitical levels. Different theoretical perspectives, such as the cultural backlash theory, the role of economic inequality within significant sectors, and predictions of the response to threat by terror management theory may help to understand the rise of governments that increase divisions and the sense of loss experienced by large groups within their jurisdiction.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".