Bibliographic record
Abstract
Abstract A few days before the Beijing Declaration and Platform for Action were unanimously adopted by the 189 countries gathered in 1995 at the Fourth UN Women Conference, a hundred Indigenous women attending the parallel NGO Forum issued their own Declaration. With this ‘Other’ Declaration, its collective authors painted a raw portrait of our world: a place where the land is simultaneously the sacred origin of everything yet remains exploited and many of its inhabitants oppressed—Indigenous peoples more than any others perhaps. The Beijing Declaration of Indigenous Women can therefore be appraised as one piece of the (so far) un-obtained radical legacy of this period, including all that it could have engendered if realized. It can further be read as the outcome of an exercise in world-making and institutional portraiture on the part of Indigenous women: with it, they painted themselves as global actors to be reckoned with. Through Anishinaabe activist Winona LaDuke’s and Inuit activist Mary Sillett’s writings relating their experience in Beijing, the reader is thus provided with a unique window into this overlooked (world/self-)portraiture whose relevance for better global governance is still unparalleled.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".