Bibliographic record
Abstract
Abstract Initially portrayed as the “great equalizer,” the COVID-19 pandemic has proved anything but. This essay recounts the sobering social disparities and vulnerabilities that the pandemic has exposed, especially when it comes to the inequalities that are baked into existing membership regimes, before turning to narratives of hope and democratic renewal. My discussion shines a spotlight on the relationship between borders, (im)mobility, and struggles for recognition and inclusion that have long been central to the practice of citizenship. Focusing on pathways to the acquisition of full membership status for those who are currently denied it, I will deploy logics and policies that have already begun to take shape in different parts of the world, with the goal of amplifying their effects and multiplying their scale. I identify three possible trajectories for postpandemic recovery, two of which offer ways to enhance equality of status and public standing by enlarging the circle of membership: first, through contribution (or what I will term “jus contribuere”), and second, by highlighting what we might call “solidarity in place.” The third reaction, which we might call the “stratification of membership,” pulls in the opposite direction by sharply redrawing the lines—legal, economic, social—that have distinguished insiders from outsiders, and exacerbated patterns of stratification and inequality of status and opportunity that predate the pandemic.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.059 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| 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".