Bibliographic record
Abstract
Children’s books disposed from the libraries can be trash, but in great varieties, once they are recycled properly, they will be treasure. There are thousands of children’s books disposed from the public libraries in the US due to different kind of reasons, through these years with careful handling, they become useful collections for the children of countries, where English is a major foreign language. They also have become valuable learning materials in schools and in the public libraries in Taiwan.In 1999, an earthquake stroke Taiwan and caused severe damages in the Central part of Taiwan. Many schools suffered from the damages. This tragedy brought in much concerns and donations from all over the world. North America Taiwan Women Association (NATWA) visited the damaged area and found that school libraries were in extreme need of help. In addition to monetary donation, NATWA realized the spiritual healing is as important as reconstruction process, therefore in 2004 NATWA launched “Turn Trash into Treasure Recycling Children’s Books” program mainly collecting off-shelf children’s books from American public libraries and shipped books to Taiwan, where the books are mostly in need. Geographically the recycling children’s books are mostly collected from about 45 public libraries in the Northern New Jersey area.This paper presents how the project was initiated and promoted, so as programs were designed to make maximum utilization of the recycled children’s books. Summarize statistical status of the project as well as cooperative process during the past four years.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".