Bibliographic record
Abstract
Writing about loneliness has been a struggle in the midst of the pandemic. Characterized by loneliness, isolation, anxiety, and fear, the COVID-19 pandemic is an exceptionally challenging time. At various points while navigating this loneliness project amid a particularly lonely time, we lamented the seeming futility of it all. A main goal of developing a Feminist Loneliness Studies in this introduction is to understand the ways that systems of oppression – white supremacy, settler colonialism, anti-queer bias, misogyny, neoliberal capitalism, and so on – create our lonely world. To date, there remains no comprehensive feminist analysis of the structural conditions that both produce and intensify experiences of loneliness. We aim to remedy this gap. That is, we seek to address what a Feminist Loneliness Studies can contribute to understanding the complexities of this complicated emotion. For example, what is the unique loneliness of the feminist killjoy who calls out, or calls in, existing forms of queerphobia, racism, and sexism? What does it mean to be a politicized person and how does that result in both alienation and isolation? What might the relationship be between white supremacy and loneliness? How is loneliness both individual and systemic, and what is the relationship between the two? What distinctive forms of loneliness are created by ableism, sanism, neoliberalism, capitalism, globalization, and the gig economy? Ought loneliness be avoided at all costs? What are the ethics of loneliness? In our introduction to this special issue, we unpack and theorize the potential perils and generative possibilities offered up by this profound emotion. Establishing a Feminist Loneliness Studies provides us with the space we need to begin addressing and comprehending loneliness.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".