Bibliographic record
Abstract
The Internet and its related technologies offer both exciting opportunities and daunting challenges for voluntary organizations such as the Canadian Historical Association. While the digital revolution has not been as transformative as some of its early prophets predicted, it has changed the habits of most academic historians who order books online, communicate with colleagues primarily by e-mail, and increasingly expect to find journal articles in cyberspace. If Google and other Internet search engines have their way, we will soon have access to the libraries of the world at the click of a mouse. In such a communications climate, the CHA has the opportunity to make its intellectual resources, in particular its journal and booklets, more accessible by going online. The challenge is to do so without undermining the structures that help to sustain our operations which are always close to the bottom line.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.221 | 0.090 |
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".