Storymaking Belonging
Bibliographic record
Abstract
Sometimes data invites more of us. To be physically held and touched, through hands creating and crafting with matter, cultivating a closer connection to the fibres, threads, textures and sinews of data. Through touching and shaping the materiality of data, other beings, places and times are aroused. Here, we share the story of data that invited more of us and how this has spurred the creation of an exhibition titled Stories of Belonging with Indigenous and non-Indigenous artist/scholars for an arts festival in Queensland, Australia. This work by the collective, SISTAS Holding Space, deeply interrogates our ontological positionality as researchers, in particular what this means in the Australian context – a colonised nation populated through waves of migration. The scars of colonization, migration and shame are held and heard through Black and White Australian women creating and interrogating belonging alongside each other – listening and holding space for each other. We air the pains of ontological destruction, silencing, disconnection and emptiness. Through experimental making research methodology, we argue the primacy of storying and making, and for provoking resonant and entangled understandings of belonging and displacement.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".