Bibliographic record
Abstract
In 1970, a Fortune magazine announced the names of top 500 enterprises and their performance in the United States. Ten years later, one third of them disappeared. Due to unprecedented technology progress and increase in competitiveness in last 20 years, so-called learning organization has become the most successful corporation. Hence, the ability to learn faster than your competitors may have become the only sustainable competitive advantage.So far schools have had difficulties to adapt enterprises’ strategy to become ‘learning school’. Using the unique characteristics and resources of a school and its community is a way to create competitive curricula. Moreover, teachers are encouraged to employ information technology (multi-media software tools) to create and edit teaching materials.In the near future, school library will play the important role to help readers to learn how to learn by software and hardware tools. Teacher librarians will set up Information Communication Technology platform, organize some reading club to improve their reading behavior, hold information literacy seed teachers contest to upgrade readers information literacy capability and researching conference.for exchanging experience Meanwhle, developing catalogue and classification standards for digital materials by IEEE’s Draft Standard for Learning Object Metadata.provide informations for their readers from Internet and knowledge database, and learn the trend of fast advance of modern science and technology to make innovative activities.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".