MétaCan
Menu
Back to cohort
Record W3127040269 · doi:10.29173/iasl7633

Turn Trash into Treasure Recycling Children's Books

2021· article· en· W3127040269 on OpenAlexvenueno aff
Amy Lin, TU Jin-wan

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsTreasureDamagesDonationBusinessHistoryPublic relationsPolitical scienceLibrary scienceLawArchaeologyComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.291
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicLibrary Science and AdministrationFrench-language works237,207