Defensive architecture and heritage education: analysis of the National Park Service and Parks Canada actions
Bibliographic record
Abstract
Defensive architecture is a heritage typology of great interest for society due to various reasons, such as its monumentality, history, beauty or ability to fascinate thanks to cinema, literature or television. Like other cultural assets, its management is based on research, preservation, restoration, didactics, dissemination and participation following current approaches. In this sense, heritage education plays a fundamental role since it is a tool that connects cultural heritage with people. This fact becomes a key aspect to guarantee its knowledge, preservation, use and enjoyment over time. This paper will analyse the actions on heritage education of the National Park Service (United States of America) and Parks Canada which are focused on defensive architecture. Both offices have been chosen because they manage examples of defensive architecture and are world leaders in heritage education. Therefore, the main purpose is to know their actions and make proposals for the Spanish context. This is an interesting fact because Spain has a rich and varied defensive architecture but heritage education still has little presence, which is surprising because heritage education favours society commitment when preserving cultural heritage. To this end, the qualitative work methodology will be used, specifically the analysis technique applied to the contents of the National Park Service and Parks Canada web pages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".