Bibliographic record
Abstract
Labour cartography is a useful frame for cartographic research. The project of labour cartography involves three main areas of study. First, recovery of an archive of maps created by workers and labour unions, a history of cartography from below. Second, mapping and spatial analysis that renders visible historical patterns of work, organizing, and working-class community life. Third, research into applications of GIS by contemporary labour unions and workers’ advocacy organizations, with an eye to developing more widespread, sophisticated, and democratic uses of maps and spatial analysis in organizing work. US labour archives contain many maps collected, repurposed, made, and distributed by workers and their unions. These maps provide the basis for a new recognition of the presence of workers and unions in cartographic history, a recognition analogous to that which guided work in labour geography that emerged in the 1990s. Extant maps illuminate scalar tensions in the production, synthesis, and dissemination of geographic knowledge. They reflect the unions’ challenge of reconciling expertise with participation: steering labour organizing activity by a range of information, from the fine-grained social geographies of the shop floor up through the broad terrain of corporate and sectoral campaign research.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".