Bibliographic record
Abstract
The doing of dis/ordered mapping/s is not about fixing lines and encounters in order to produce a map or model; on the contrary it is about exploring differing embodiments and material relations among people, things and disability. New narratives flow out through the actions of the fingers and body by doing collaborative mappings through embodiment. Narratives are of/from particular places and are mapped out through particular embodied experiences. It is through the doing of collaborative fibre mappings to analyse embodiment that we realized how much the materiality of things within two national museums in Canada pressed and pushed upon us. Through our struggle of giving in to and following materiality, we realized that doing of mapping/s are continual performances of making, unmaking, doing and undoing through people’s embodied abilities. The complex braiding of this research suggests a need for a more holistic exploration of inclusion in museum spaces through an embodied articulation because users are moving and performing unconsciously with their bodies at all times. Through a doing of dis/ordered mappings alternative ways of approaching, framing, doing and narrating interior spaces open up new knowledge processes, engagements, methodologies and methods through an embodied criticality that allows for a kind of stumbling and playing across/with new encounters.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 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".