Building A Fire: The Geographies Of Community Geography
Bibliographic record
Abstract
This paper contributes to scholarly conversations about how to (not) define community in community geography (CG). We draw on Annemarie Mol and John Law’s formulation of a fire topology to reflect on CG research spearheaded by a community-based environmental organization concerned with industrial contamination in northeastern Oklahoma. To explore how, where, and why we came together around a multimedia storytelling initiative aligned with the geohumanities, we trace the events and encounters leading to our collaboration. We then closely examine one of the first digital products to emerge out of our relationships and research: a StoryMap detailing the history and environmental impacts of a BF Goodrich tire factory that operated between 1946 and 1986 in Miami, the county seat of Ottawa County, Oklahoma, while also commemorating the labor and lives of people associated with the plant. Our overview of the StoryMap and its creation also commemorates the geographies of the embodied work experiences in building community around the research informing the StoryMap. Our discussion considers the dynamic and sporadic dimensions of our ongoing CG research, celebrating accomplishments and potential for future endeavors without failing to recognize how the quotidian friction of distance, as well as professional commitments, have stymied or slowed—but not stopped—our collaboration. Keywords: collaboration, StoryMap, Superfund site, environmental activism, geohumanities .
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".