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In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

To understand how parents make the decision to implant their deaf young children with cochlear implants, focusing specifically on the concepts of normality, medicalization, and stigma.

I conducted 33 semi-structured interviews with the hearing parents or parent of children with cochlear implants. In all but two families I interviewed the primary caretaker which in all cases was a mother. In the remaining two interviews, I interviewed both parents together. Because of the relative scarcity of families with children with cochlear implants, and the difficulty in connecting with these families, I used a convenience sample, and I did not stratify it in any way. The only requirement for parents to be interviewed is that they had at least one deaf child who had been implanted with at least one cochlear implant. Although this is a small sample, the findings are transferable to other families with the same sociodemographic characteristics as those in my study.

Parents in the study focused on three key concepts: normality, risk analysis, and being a good parent. Dispositional factors such as the need to be “normal” and the desire for material success for one's children appeared to moderate the cost-benefit calculus.

This interview project concentrated on hearing families who had implanted their deaf children with cochlear implants; it does not include culturally Deaf parents who choose to use American Sign Language (ASL) with their Deaf children. Understanding how Deaf families understand the concepts of normality, medicalization, and stigma would shed light on how a distinctly “abnormal” group (by a statistical conception of normal) – ASL-using Deaf people-explain normality in the face of using a non-typical communication method. One can learn a lot by studying the absence of a phenomena, in this case, not implanting children with cochlear implants. It is possible that the existential threat felt by some Deaf people, specifically the demographic problem presented by cochlear implants, led Deaf educators or parents to resist being the subject of research.

Overwhelmingly the sample was female, and white. Only two participants were male, and none of the participants were non-white. The lack of diversity in the sample does not necessarily reflect a lack of diversity of children receiving cochlear implants. Medicaid, which disproportionately covers families of color, covers cochlear implants in most cases, so low SES/racial intersectionality should not have affected the lack of diversity in the sample. However, the oral schools are all private pay, with few scholarships available, so low SES/racial intersectionality in the sampling universe (all children who attend oral schools), may have played a part in the lack of racial diversity within the sample.

Parents in this study were very specific about the fact that they believed cochlear implants would lead to academic, professional, and personal success. They weaved narratives of normality, medicalization, and stigma through their stories. Normality is an important lens from which to see stories about disability and ability, as well as medical correction. As medical science continues to advance, more and more conditions will become medicalized, leading to more and more people taking advanced medical treatments to address problems that were previously considered “problems with living” that are now considered “medical problems” that can be treated with advanced science.

This chapter's contribution to the sociological cochlear implant literature is it's weaving of narratives about normality, stigma, and medicalization into parental stories about the cochlear implant decision-making process. Most literature about the cochlear implant decision-making process focus on cost-benefit analysis, and logical decision-making processes, whereas this paper focuses on decision-making factors stemming from bias, emotions, and values.