Intersectionality and humanity: A keynote for “remapping the feminist global”
Bibliographic record
Abstract
That Modern Feminist Thought shares its lineage with the liberal Enlightenment and Humanism has continued to render certain feminist practices assimilable to the colonial-racial regime of knowledge that has long underpinned what we normatively uphold as political modernity and its foundational assumptions. This article, originally presented as a conference keynote address, revisits the familiar feminist aporia through exploring the multiple genealogies of feminisms to highlight their theorizations of difference, the human, and other ways of being and caring. It also reconsiders how “intersectionality” – a concept originally forwarded as an alternative legal doctrine yet extensively deployed for various political ends such that the term appears to have lost its original relevance for some – can be repurposed as a critical methodology with which to challenge the universalism of liberal humanism/feminism and the attendant compartmentalization of academic knowledge, but ultimately, to strengthen coalitional possibilities. The article addresses how some of the largely North America-based conversations can resonate transnationally and effectively with some of the most urgent feminist engagements that have unfolded across and beyond plural “Asias,” however imagined.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.058 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".