With the rapid expansion of academic literature and the proliferation of
preprints, researchers face growing challenges in manually organizing and
labeling large volumes of articles. The NSLP 2024 FoRC Shared Task I addresses
this challenge organized as a competition. The goal is to develop a classifier
capable of predicting one of 123 predefined classes from the Open Research
Knowledge Graph (ORKG) taxonomy of research fields for a given article.This
paper presents our results. Initially, we enrich the dataset (containing
English scholarly articles sourced from ORKG and arXiv), then leverage
different pre-trained language Models (PLMs), specifically BERT, and explore
their efficacy in transfer learning for this downstream task. Our experiments
encompass feature-based and fine-tuned transfer learning approaches using
diverse PLMs, optimized for scientific tasks, including SciBERT, SciNCL, and
SPECTER2. We conduct hyperparameter tuning and investigate the impact of data
augmentation from bibliographic databases such as OpenAlex, Semantic Scholar,
and Crossref. Our results demonstrate that fine-tuning pre-trained models
substantially enhances classification performance, with SPECTER2 emerging as
the most accurate model. Moreover, enriching the dataset with additional
metadata improves classification outcomes significantly, especially when
integrating information from S2AG, OpenAlex and Crossref. Our best-performing
approach achieves a weighted F1-score of 0.7415. Overall, our study contributes
to the advancement of reliable automated systems for scholarly publication
categorization, offering a potential solution to the laborious manual curation
process, thereby facilitating researchers in efficiently locating relevant
resources.

本研究报告介绍了基于预训练语言模型的转移学习方法在学术文献分类中的运用。通过丰富数据集、使用不同的预训练语言模型，并调整超参数，研究发现 fine-tuning 预训练模型可以显著提高分类性能，其中 SPECTER2 模型效果最佳。此外，将额外的元数据加入数据集，尤其是来自 S2AG、OpenAlex 和 Crossref 的信息，可以进一步提高分类结果。本研究对于构建可靠的自动化学术出版物分类系统具有重要意义，为研究人员高效定位相关资源提供了潜在解决方案。

增强的 BERT 嵌入用于学术出版物分类

Enriched BERT Embeddings for Scholarly Publication Classification

Preprints play an increasingly critical role in academic communities. There
are many reasons driving researchers to post their manuscripts to preprint
servers before formal submission to journals or conferences, but the use of
preprints has also sparked considerable controversy, especially surrounding the
claim of priority. In this paper, a case study of computer science preprints
submitted to arXiv from 2008 to 2017 is conducted to quantify how many
preprints have eventually been printed in peer-reviewed venues. Among those
published manuscripts, some are published under different titles and without an
update to their preprints on arXiv. In the case of these manuscripts, the
traditional fuzzy matching method is incapable of mapping the preprint to the
final published version. In view of this issue, we introduce a semantics-based
mapping method with the employment of Bidirectional Encoder Representations
from Transformers (BERT). With this new mapping method and a plurality of data
sources, we find that 66% of all sampled preprints are published under
unchanged titles and 11% are published under different titles and with other
modifications. A further analysis was then performed to investigate why these
preprints but not others were accepted for publication. Our comparison reveals
that in the field of computer science, published preprints feature adequate
revisions, multiple authorship, detailed abstract and introduction, extensive
and authoritative references and available source code.

计算机科学领域的预印本在 arXiv 上发布后，通过引入基于语义的映射方法（使用了 BERT），我们发现所调研的预印本中的 66％发表时标题未变且 11％具有改变标题和其他修改。进一步的比较研究揭示了在计算机科学领域中，被发表的预印本具有充分的修订、多个作者、详细的摘要和导言、广泛且权威的参考文献以及可用的源代码。

计算机科学 arXiv 上的预印本有多少被实际打印了，以及为什么

How many preprints have actually been printed and why: a case study of  computer science preprints on arXiv

Articles in high-impact journals are, on average, more frequently cited. But
are they cited more often because those articles are somehow more "citable"? Or
are they cited more often simply because they are published in a high-impact
journal? Although some evidence suggests the latter, the causal relationship is
not clear. We here compare citations of preprints to citations of the published
version to uncover the causal mechanism. We build on an earlier model of
citation dynamics to infer the causal effect of journals on citations. We find
that high-impact journals select articles that tend to attract more citations.
At the same time, we find that high-impact journals augment the citation rate
of published articles. Our results yield a deeper understanding of the role of
journals in the research system. The use of journal metrics in research
evaluation has been increasingly criticized in recent years and article-level
citations are sometimes suggested as an alternative. Our results show that
removing impact factors from evaluation does not negate the influence of
journals. This insight has important implications for changing practices of
research evaluation.

比较预印本和已发表论文的引用情况，推断高影响期刊对引用量的因果效应。发现高影响期刊不仅选择引用量更高的文章，同时还提高文章的引用率。研究表明，即使不考虑影响因子，期刊在研究评估中的影响仍然存在，对研究评估实践的改变具有重要影响。