Writing multi‐vocal intersectionality in times of crisis
Bibliographic record
Abstract
Abstract This article is a multi‐vocal account, a form of writing differently , which captures our changing lives and livelihoods under the present global health crisis. Through the process of writing, we create a safe space to understand how the COVID‐19 pandemic exposes our gendered, intersectional lives. Our writing gives voice to suppressed thoughts and embodied affects as they surface in relation to entrenched structural inequalities where we witness the marginalization of intersectional difference, in our case women, the feminine, and race in academia and neoliberal society. By rendering visible the structural inequalities that have become amplified during the pandemic, and the ways in which these inequalities have affected our everyday lives, we are able to give witness to intersectional differences. Our multi‐vocal embodied text is offered as an emancipatory, affective mobilization of our lives, encompassing feelings of grief, loss, fear, anger, frustration, and vulnerability. This collective piece of writing gives rise to solidarity in a crisis‐stricken world where we choose to live with hope.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".