Strata-Mapping the Detroit River Border with the Hamilton Perambulatory Unit
Bibliographic record
Abstract
The Hamilton Perambulatory Unit (HPU)’s strata-mapping framework is an experimental research-creation practice that focuses on how spatial meaning is created through a performative “stratigraphic” sensing and researching of a site. The international border between Detroit, Michigan and Windsor, Ontario makes an especially compelling site for experimental cartographies in light of the conflicts over borders and walls in the current political environment. At the southernmost tip of the Great Lakes system, we focused our attention on this river border as a material site and geopolitical space: it enabled us to investigate alternate possibilities for sensing and envisioning the layered and conjoined histories of this fluid space. The Ojibwe name for this location is waawiiatanong ziibi, “where the river bends,” suggesting a radically different spatial imaginary than the divided space that has been established through colonial and national histories. Experimental cartographies can thus help to develop alternate ways of experiencing such sites, an initial step towards decolonizing the spatial imaginary through a project of delinking. In September 2018, we conducted a workshop entitled Buoyant Cartographies, focusing on a performative and intermedial investigation into spatial meanings and their construction on Peche Island, which sits in the middle of the Detroit River. This was one of three Detroit River sites investigated in the workshop, with contributions from workshop organizers and HPU co-conspirator Donna Akrey.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".