The assessed value of cultural destinations in Toronto
Bibliographic record
Abstract
Theory suggests many benefits to a city from cultural destinations (CDs). This study offers a framework for evaluating CDs through studying visitors to CDs and visitors to other, less cultural destinations, which we call retail destinations (RDs). As each destination represents differing positions along our “cultural continuum,” we examine how visitors perceive the destinations and report their behaviors in each. We selected three CDs and three RDs in Toronto, surveying 30 adults in each destination about their behavior and impressions. Our findings show significant differences between the respondents in the CDs and the RDs. Some findings resonate with existing theory, in that CD respondents were more likely to be highly educated and White, non-Hispanic. However, other findings suggest avenues for examination, in that CD respondents did not report greater wealth or spending than did the RDs but did mention socializing as the primary reason for visiting a CD more so than those visiting an RD. These findings suggest that by encouraging CDs, cities may improve the quality of life for residents and visitors. Thus, cities might do well to use their municipal policy and economic development tools to drive revenues to cultural destinations or to foster CD uses in retail destinations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".