The State and History of the MEMORY at Home and Abroad
Bibliographic record
Abstract
Memory of the World Programme is initiated by UNESCO.Then a lot of MEMORYs turn up.MEMORY is the project to salvage the documentary records from gradual aging,damage,even disappearance by using the best technology so that the human memory could be more complete.Digital and network-based memory is an important part of the project.Canada has no MEMORY directly named with a very high level;MEMORY of the United States pays attention to education and to promote learning;MEMORYs in China came later so that there are gaps between Chinese one and the world advanced level MEMORYs.And they are unsuitable with the China's increasingly strong position in the world and the ancient civilization.However,memories of Beijing,Taiwan's memory and the nongovernmental MEMORYs are very unique.Since the globalization today,the Offhand Multi-language Switching and the Cross-language Information Retrieval have become very difficult technologies in MEMORYs.The authors developed a dynamic multi-language solution: to set the output of the language on the web-page as variables,to evaluate the variables according to the user's language,then to be able to immediately output of information in different languages.In cross-language information retrieval,the authors get both the quick response to the user and the find of the accurate information based on the controlled terms with the combination of the question method and the controlled terms.Those technologies have been used in Macao memory and gotten very satisfactory results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".