People, Places, and Culture: A Tool For The Mapping Of Community Cultural Asset
Bibliographic record
Abstract
The mapping of a community's cultural assets can be an important tool for directing community action for protection, safeguarding and developing what are, in many cases, some of the most valuable assets a community has for development and supporting quality of life. These resources can comprise aspects of tangible heritage such as historic places, cultural landscapes, structures, and collections along with Intangible Cultural Heritage that includes stories, cultural traditions, traditional knowledge and practices, and individuals who were known for their special knowledge and skills. Heritage NL's program, "People, Places & Culture" supports communities in Newfoundland and Labrador to map their cultural heritage assets and to plan for their protection and development. It comprises two workshops in which interested members of a community come together to place their tangible and intangible heritage on a map and then plan for protection and development. The exercise allows a community to: a) think beyond buildings and material aspects of their heritage; b) identify clusters and themes that may emerge that highlight what is unique or special. As an example, in the Mi'Kmaq community of Flatbay on Newfoundland's west coast, a strong tradition of guiding along with an intimate knowledge of the landscape surrounding the community suggested opportunities for developing new tourism opportunities. Heritage NL often follows up with additional meetings that explore how a community might organize itself to take action to protect, safeguard and develop its heritage resources or with assistance for undertaking oral history workshops or heritage inventories. The proposed session would highlight the "People, Places, and Culture" program with examples from communities in which the Foundation has worked to date, along with some of the issues and challenges that communities face in taking action.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".