Bibliographic record
Abstract
It has become a trend, adopted by postsecondary institutions to strengthen their commitment to ‘diversity’ and ‘inclusion’. With growing attention to issues of social and systematic marginalization, intersectionality is an unavoidable term within campus discourse. Though the term’s increasing popularity is welcomed both in curricular and co-curricular settings, there still exists considerable confusion concerning what the concept actually means and how it can or should be applied in feminist inquiry. This poster demonstrates my exploration of how feminism as a political site for whiteness, with no room to make sense of the experiences of women of colour has misused the term intersectionality. In doing so, I have differentiated between intersectionality as theory of oppression and power, and intersectionality as a theory of difference. I have responded to the issue of whether the use of intersectionality in contemporary feminism, as a theory of difference is still useful in framing the oppression of women of colour or whether it has become a dangerous shortcut, made in order not to continue the process of deeply reflecting on our complicated relations. The conclusion drawn is that intersectionality, as understood as simply a theory of difference creates forced categories of identity, displacing and further marginalizing members of minority groups. My findings mean that though the intended used of the term was a powerful critique by women of color about systems blind to their oppressions, it has been repurposed by contemporary feminists to group the experiences of women of colour in contrast to dominant white female identities.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.060 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".