We Have to Change to Stay the Same - The Maritime Museum of the Atlantic (Nova Scotia, Canada)
Bibliographic record
Abstract
In many ways, it seems that 2022 is a year for renewal. A re-start from a period of closures and other impacts of a devastating pandemic, and from a period of heightened global awareness on social injustice and inequality, racism, increasing threats of war, climate change, species at risk, and ocean conservation. The Maritime Museum of the Atlantic (MMA) hosted the 50th Anniversary International Congress of Maritime Museums (ICMM) in this renewal year offering a robust programme that was heavily influenced by these shared concerns. The combination of embarking on a new half-century, and an unprecedented awareness of significant global issues, provided the obvious opportunity to explore and re-imagine our roles as meaning-making institutions, as impactful maritime museums. Many museums, the MMA included, had already been taking an inward look, trying to understand the impacts of decades of institutional bias and the reality of being part of the climate crisis. So much had changed during the past few years. It had been a long time since we could gather in-person and experience the kind of powerful networking that can only happen face-to-face. I think we had a tremendous appetite to share and discuss our recent experiences and ambitions. The moment was right for a Congress focused on new beginnings and growth. As the saying goes, “Calm seas do not make a good captain,” and we had all been through some pretty rough seas.
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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.076 | 0.022 |
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".