Moving Together Towards Collective Access (Dispatch)
Bibliographic record
Abstract
We Move Together (WMT) (Fritsch et al., 2021) is a picture book that follows a mixed-ability group of children as they move around their city community.We see the children move in different ways -fast, slow, using their feet as well as wheelchairs, scooters, crutches, bikes, and canes.Along the way, the kids encounter a great many people and animals, as well as a number of obstaclescommunication barriers, inaccessible infrastructure, political disagreements, etc. -that they work together to navigate.With regard to these obstacles, sometimes the work of moving together looks like building ramps, pivoting to a more inclusive game, or learning American Sign Language (ASL).Other times, this work looks like slowing down, sitting with disagreement, or taking a break.It is in part through navigating these challenges that the children encounter, relish in, and in turn grow disability community and culture.The children's commitment to moving together -their desire for collective as opposed to individual forms of access -deliver them into new political and artistic spaces, justice-oriented cultural formations that center disabled life, celebrate disabled joy, and encourage the flourishing of the widest array of bodies, minds, and movements.In Figure 1 the children can be seen at the center of such a flourishing: an abundance of disabled life and politics.It is as much a celebration of how our individual bodies and minds are and move, as it is a fulcrum of social movements moving together in solidarity, dreaming of, and fighting for better futures for all of us.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.107 | 0.021 |
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".