It’s dangerous to go alone! How about *we* do this!?
Bibliographic record
Abstract
We’re all worried about preserving digital assets at some level. One of the most concerning parts of this process is the storage component, and as new and larger objects and collections come under libraries’ care, the pressure to find long-term storage solutions is growing. There are a lot of solutions vying for mind-share — many of them commercial — but it’s unclear the extent to which these solutions really represent sustainable alternatives. But what if there were a way for us to do this? Not “us” you or me, but “us”: you *and* me. What if “us” is all of us in the country? What does that mean for sustainability, and the future of trustworthy repositories? If we could do this, what might it look like? Are there models in place elsewhere that might help inform what this Canadian solution might look like? This session will discuss issues around sustainability with a variety of commercial storage options and compare those with the significant potentials created by a series of recent, open-source driven, developments in the hardware and software worlds. We’ll talk about what software and APIs are available to make a locally created storage network accessible to your applications, and will demonstrate how you can build a scalable community cloud storage solution using commodity hardware that allows for middleware replication, geographic diversification, object storage using a common API, and remote management.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.022 | 0.038 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.124 | 0.121 |
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".