Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a digital museum ethnographer, I would like to devote this chapter to sharing my personal experience in addressing ethical considerations while conducting research on museum visitors’ behavior in online spaces. My research looks at online museums as important sites of cross-cultural communication. These sites project powerful political and cultural messages across borders and engage not only local but predominantly international audiences. Captivated by the diversity of online museum programs that connect people across the globe, opening up virtual spaces for cross-cultural learning, and immersing online visitors into educational experiences, I traveled the world to conduct a number of case studies. I researched digital spaces of large international museums in Canada, the United States, the United Kingdom, Australia, and Singapore. My ethnographic research revealed that museum online communities as social interactive worlds can be powerful tools of cultural representation or mis-representation, sites of memory and identity construction, and building citizenry or political battlegrounds of resistance and social riots. Online museums can build unique “bridges” among communities for improving intercultural competence and tolerance or, in contrast, can invoke religious and cultural wars. These insights and findings were possible due to immersive ethnographic research within different digital museum spaces. I explored various online museum communities and collected and analyzed a large amount of textual and visual data demonstrating various behaviors of online “museum goers.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it