The Quantitative Analysis of Science Foundation Support and Paper Output of APEC(Asia-Pacific Economic Cooperation)Numbers in the Field of Library & Information Science——Based on the Platform of Web of Science
Bibliographic record
Abstract
Based on the platform of Web of Science, this paper carries out the data investigation and quantitative analysis on the funded papers from 2008 to 2014(incomplete statistics)in the field of library information science of APEC members in order to provide some useful references for the perfection and development of China's scientific foundation system and the formulation of supporting strategies in library information science. The research on the indicators such as the ratio of funded papers, the total cited frequency, the average cited frequency per paper, and the number of donors per paper etc. finds that in the field of library information science,the ratio of funded papers of APEC members is between 6.58%~50.00%, and the average cited frequency per paper is between 1.00~6.91; USA, Canada and Australia have bigger quantities of funded papers, lower ratio of funded papers and higher average cited frequency per paper; China, Korea and Chinese Taiwan have bigger quantities of funded papers, higerh ratio of funded papers and lower average cited frequency per paper; the multilateral funding phenomenon is fairly common, but one or two donators usually play the leading role; there is abnormal distribution phenomenon of the citation of funded paper.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".