WIDENING AUDIENCES – MAKING HERITAGE RECORDING DATA EASILY ACCESSIBLE VIA HTML APPLICATIONS
Bibliographic record
Abstract
Abstract. With the ever increasing size and complexity of heritage recording datasets, and consequently, the required expertise to manipulate and extract information for conservation projects from this data, the use of dissemination tools was researched and used to help bridge the gap between information gatherers and users in order to increase accessibility and utilization. This paper examines a variety of case studies where dissemination tools were utilized to make heritage recording data more easily accessible for a variety of users. The first example involves high resolution photography; the second explores methods of sharing large point cloud datasets; the third explores panoramic photography and dissemination via virtual tours; and the fourth, capitalizes on using panoramic images as a by-product of terrestrial laser scan data. All data was disseminated solely through the use of HTML outputs, ensuring that the end users did not require any specialized software and minimal to no training to visualize, manipulate and extract data from the assembled information. Collectively the project team felt that the simplicity of these outputs would increase the likelihood of their utilization by the various user groups. Based on the teams past experience, the requirement for specialized software greatly diminished the chances of broad use of the data by untrained individuals. By adopting a HTML platform, the difficulties wrought by software installation restrictions imposed on many organizations or limited by access to required hardware, could be greatly diminished. There is also a possibility for this type of data to be disseminated to the public for their interest using these tools; however, the presented examples show only how these methodologies were used in the understanding phase and execution stages by other professionals.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".