Eternity revisited: in pursuit of a national documentation strategy and a national archival system
Bibliographic record
Abstract
As I sit down to write this thought piece in January 2014, our Canadian colleagues are preparing for a Canadian Archives Summit with the enviable title ‘Towards a New Blueprint for Canada’s Recorded Memory’.1 To me the most interesting word in this title is the word ‘new’. While the Canadian summit has been organised in response to the crisis associated with the removal of federal funding for their National Archival Development Program, Australians can nevertheless only look with wonder at another Commonwealth country with a federal system of government that has the luxury of an existing national blueprint for recorded memory – for surely one cannot create a ‘new’ blueprint if an old one does not already exist.
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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.037 |
| Scholarly communication | 0.041 | 0.030 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".