#afFEMation – Demonstrating a framework for gender equitable histories
Bibliographic record
Abstract
The #afFEMation website is designed to raise the visibility of women who have made significant contributions to Australian graphic design since 1960. The resulting archive of 24 women–alongside their biographies, photographic portraits, galleries of work and audio/visual interviews–has become part of a movement which is attempting to fill gendered gaps in historical narratives and archives. This patriarchal perspective of our past excludes and devalues the significant contributions made by women–misrepresenting our view of the world. The omission of women from history, systematically elevates the visibility and voice of men. Privileged assumptions and narrow measures of inclusion distort the process of documenting diversity amongst those who deserve notoriety. However, this article seeks to encourage this momentum towards social inclusion, by proposing a framework of historisation that seeks to eliminate these gender inequities. The effectiveness of this framework to affect social change, is demonstrated through its implementation in #afFEMation and implies a usefulness in broader disciplines where success has often become an immeasurable and subjective quantifier. The framework consists of five points that systemize privilege checking, measure gender equity, validate inclusion through triangulation, rejects the urge to reference women in relationship to men and prioritises recent histories.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".