Bibliographic record
Abstract
The profession of digital archivist is crystallising, fundamentally challenging traditional archival roles. The very nature of digital records also challenges the sustainability of archival systems and collections. Records that used to stay stable for decades in an analogue world now risk being lost or damaged within moments of creation. How should archivists react to these changes? Archivists have to lift ourselves out of our analogue environment and focus more effort on forging a new path, to reposition archives, archival institutions and archival practitioners more strategically for the future. To do this, archivists must resist the temptation to think that we and we alone – as people, as archivists or as today’s archivists as opposed to yesterday’s archivists – can come up with the ultimate solution to the world’s recordkeeping problems. Archivists must keep innovating, absolutely. But we also need to be agile and flexible, remembering that anything we come up with today will be superseded at some point in the future – increasingly, in the very near future. Archivists need to forge links with archives, systems and people in order to come up with approaches to records and archives care that remain usable now and flexible well into the future.
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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.034 |
| Scholarly communication | 0.035 | 0.054 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".