“I <scp>DON</scp>’T <scp>WANT MY TOWN TURNED INTO</scp> A <scp>SPECTACLE</scp>”: Community Museums as Tactics
Bibliographic record
Abstract
Abstract The former silver mining town of Cobalt, Ontario (pop. 1,128), has four history museums. The museums, created at different times and by different constituencies, reflect citizens’ ongoing efforts to revive the town and imagine its urban future. Over the past 50 years, residents have also engaged in multiple attempts to establish the town as a mining heritage tourism destination and/or to reestablish mining. Although the museums are implicated in both aspirations, we argue that they work to situate the memory work of local history within discrete spaces. While the museums have been recruited to support tourism and heritage efforts, they sit uncomfortably within grander schemes to remake the town as a tourist destination. Indeed, we argue that the museums serve as tactics, per de Certeau, to contain and restrict tourism‐oriented development. As vernacular spaces where residents have recorded local histories, the museums serve as statements about both what the town should be and what it should not be.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".