Bibliographic record
Abstract
Seven years after Laura Millar's eloquent and wide-ranging book was first published, it is ever more apparent that in future the great majority of records will be created and used in digital form. At present, most record-making environments are hybrid – to varying extents, paper records continue to be created and kept alongside their digital counterparts – but the balance is firmly shifting towards the digital. Organizations are now disposing of their filing cabinets at an unprecedented rate. Even if the wholly paperless office may still prove to be a chimera, the ‘less-paper’ office is now a visible reality. It has also become clear that archivists will very soon face, if they are not already facing, a digital deluge. The world is creating massive amounts of digital content, and the archivists of the future will encounter quantities of records that exceed anything that archivists have experienced in the past. In this age of digital abundance, human society will still look for evidence of, and information about, actions that have been undertaken, events that have occurred, decisions that have been made, and rights that have been protected, abused or amended. Records and archives will still be needed, and the long-standing archival principles that Millar expounds will be no less valid, but the methods and techniques required to put those principles into practice will often be very different.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".