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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.16546 |
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| _version_ | 1866913805568573440 |
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| author | Zhang, Bolun Shen, Yang Li, Linzhuo Ji, Yu Wu, Di Wu, Tongyu Dai, Lianghao |
| author_facet | Zhang, Bolun Shen, Yang Li, Linzhuo Ji, Yu Wu, Di Wu, Tongyu Dai, Lianghao |
| contents | The ascent of scaling in artificial intelligence research has revolutionized the field over the past decade, yet it presents significant challenges for academic researchers, particularly in computational social science and critical algorithm studies. The dominance of large language models, characterized by their extensive parameters and costly training processes, creates a disparity where only industry-affiliated researchers can access these resources. This imbalance restricts academic researchers from fully understanding their tools, leading to issues like reproducibility in computational social science and a reliance on black-box metaphors in critical studies.
To address these challenges, we propose a "tinkering" approach that is inspired by existing works. This method involves engaging with smaller models or components that are manageable for ordinary researchers, fostering hands-on interaction with algorithms. We argue that tinkering is both a way of making and knowing for computational social science and a way of knowing for critical studies, and fundamentally, it is a way of caring that has broader implications for both fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tinkering Against Scaling Zhang, Bolun Shen, Yang Li, Linzhuo Ji, Yu Wu, Di Wu, Tongyu Dai, Lianghao Computers and Society Human-Computer Interaction The ascent of scaling in artificial intelligence research has revolutionized the field over the past decade, yet it presents significant challenges for academic researchers, particularly in computational social science and critical algorithm studies. The dominance of large language models, characterized by their extensive parameters and costly training processes, creates a disparity where only industry-affiliated researchers can access these resources. This imbalance restricts academic researchers from fully understanding their tools, leading to issues like reproducibility in computational social science and a reliance on black-box metaphors in critical studies. To address these challenges, we propose a "tinkering" approach that is inspired by existing works. This method involves engaging with smaller models or components that are manageable for ordinary researchers, fostering hands-on interaction with algorithms. We argue that tinkering is both a way of making and knowing for computational social science and a way of knowing for critical studies, and fundamentally, it is a way of caring that has broader implications for both fields. |
| title | Tinkering Against Scaling |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.16546 |