Bibliographic record
Abstract
In Canada, as elsewhere around the world, government is trying to move into the Internet Age, to communicate more and more interactively with an everincreasing portion of the electorate and to increase the interoperability of digitized media. The Canadian Government Online Initiative, of which we are a part, is an example of this trend. To facilitate access to our materials, we need metatags, metatags that, by and large were originally set up to deal with print media. Thus, we have been struggling in recent years to apply metadata to a test database of Canadian cultural audio and visual clips that we call Heritage Line. We have followed many avenues for making our data searchable and accessible. We have used the Dublin Core schema, both with the qualified set of elements as well as the unqualified set. Our problems arose specifically with respect to the elements 'type' and 'format'. Mpeg-7 currently appears to offer a solution to our problem.
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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".