Museums in British Columbia During the COVID-19 Pandemic: Continuing to Engage the Public Online
Bibliographic record
Abstract
The COVID-19 pandemic forced British Columbia museums to close to the public for a period of time in 2020. As traditional museum engagement takes place in person at museums, many B.C. museums transferred some engagement to be delivered virtually. This research paper explores past literature that has statistically analyzed museums’ use of Instagram as an engagement tool and discusses four surveys of museums’ experiences during the pandemic. Three case studies of B.C. museum professionals’ experience doing online engagement during the pandemic were conducted and found that a key success to online engagement is trying and learning as you go. The quantitative analysis determined that B.C. museums increased the number of Instagram posts published during the pandemic; however, medium/large museums increased their Instagram activity when compared to small museums. Further, in the quantitative analysis types of Instagram posts were analyzed to determine that Call to Action posts gain more engagement than Promotional posts. Call to Action posts were further analyzed through speech act theory to find that these posts require a deeper level of consideration and targeting to have an effective perlocutionary response from the audience. This research concludes that online engagement is important for museums to continue to connect with their audiences and that consideration of the type of Instagram posts published is needed to ensure they foster meaningful engagement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".