Investigating the use of a web-map survey tool for heritage planning
Bibliographic record
Abstract
Heritage planning is critical for preserving places of value to community members. Citizen participation is necessary so that the public can have a voice in matters that directly impact their own communities. Public participation has traditionally been in the form of public meetings, workshops, interviews, analog surveys, and questionnaires. However, often only a subset of local residents take part in these physical means of participating in their local community’s decision making. There is a need for the use of web-mapping for gathering citizen input. This study investigated how map-based survey tools can support public participation in built heritage planning in Stratford, Ontario using a web-map tool called Heritage Planner. The main functionality of Heritage Planner was to use its web-map and survey capabilities together to consider heritage value at property- and neighbourhood scales. Due to the Covid-19 pandemic, citizens could not be recruited from Stratford. Instead, students from the Environment Faculty at the University of Waterloo were recruited to provide feedback on the app. Participants who had not visited Stratford before were more inclined to comment on the larger sized properties in the city, while participants who had visited the city before were more inclined to comment on properties influenced by the neighbourhoods they visited. Due to the limitations in this study, the main direction to take for future research would be to implement an improved Heritage Planner app amongst citizens in Stratford and implement similar studies in Ontario and Canada.
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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 | 0.004 | 0.016 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".