Bibliographic record
Abstract
There is much to be learned through sharing stories of resistance to binaries. In this paper I introduce myself (so that you know a bit about how my experiences with privilege and marginalisation shape my view of the world and the knowledge which I create). I share some stories from my own life (and gifted to me) that make visible how binaries reinforce oppression, particularly how binaries reinforce colonial ideas about ‘superiority’. I share news stories about two Indigenous change-makers, so we can dream about how to resist binary and how to support each other with the lonely work of being a bridge/veggie burger with bacon. I’m a teacher, so sometimes I invite you to check out my references to learn more about vital concepts that cannot be explored within this one paper. I include my entire self in this writing, through the inclusion of my humour, my heart and my voice. I have found the most belonging with communities who are comfortable existing in a world where sacred and profane have a lot of overlap. Usually this means communities who exist on the margins, which is a great place to engage in binary challenging praxis! It is a sacred act to keep my outraged profane voice, particularly in communities such as ‘professionals’ where this voice is often looked down upon, even as we claim to centre these voices. The part of me who says ‘fuck’ is the same part of me who expects justice for all, so shutting them up is impossible anyway!!
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".