ABSTRACT
The “publish and flourish” culture in the biomedical field has led to an increase in the number of publications worldwide, creating pressure on researchers to publish frequently. However, this focus on quantity over quality has resulted in an inflation of the number of authors listed in articles, leading to authorship issues and the rise of fraudulent or predatory scientific and medical journals. To maintain the credibility of scientific research, it is necessary to reform the publication metrics and explore innovative ways of evaluating an author’s contributions. Traditional metrics, such as publication counts, fail to capture the research’s quality, significance, and impact. As a result, this viewpoint explores and highlights different metrics and novel methods by which an author’s productivity and impact can be assessed beyond traditional metrics, such as the H index, i10 index, FWCI, HCP, ALEF, AIF, AAS, JIF, CNA, awards/honors, citation percentile, n-index, and ACI. By using multiple metrics, one can determine the true impact and productivity of an author, and other measures such as awards and honors, research collaborations, research output diversity, and journal impact factors can further aid in serving the purpose. Accurately assessing an author’s productivity and impact has significant implications on their academic career, institution, and the broader scientific community. It can also help funding agencies make informed decisions, improve resource allocation, and enhance public trust in scientific research. Therefore, it is crucial to address these issues and continue the ongoing discussion on best method to evaluate and recognize the contributions of authors in today’s rapidly changing academic landscape.
KEY WORDS: Altmetrics, bibliometrics, biomedical research, citation, citation metrics
“Publish or perish” has become the sole motive for biomedical researchers seeking academic success, thanks to the simple and rapid publication made possible by digital advancement. Whether a researcher is a novice or a recognized expert, their academic development is indicated by their publication metrics. This has been seen from the recent upsurge of biomedical literature.[1]
This culture is triggered not only by the open access movement and the ease of publishing online but also by academic institutions, governmental agencies, and universities placing excessive emphasis on the quantity of publications as a gauge of an author’s productivity and impact.[2,3]
Researchers are under tremendous pressure to publish more often as a result of this trend, which could result in emphasizing quantity above quality.[4] The pressure to publish or perish has resulted in an inflation of the number of authors listed in articles, giving rise to authorship issues and the rise of fraudulent or predatory scientific and medical journals.[5,6]
These journals exploit the “publish or perish” culture by promising an expedited publication and minimal peer review in exchange for publication fees. This tradition negatively impacts scientific quality of literature and lures young biomedical researchers towards unlawful scams and fraudulent practices. These journals merely care about generating profits and lack any form of scientific peer review, can pollute the scientific literature by publishing erroneous and inaccurate research, thus undermining the fundamental goal of evidence-based decision-making.[7]
There is a long-sought need for establishing appropriate and standardized measures for assessing an author’s research impact and productivity. This objective is met by considering the number of publications a researcher authors in his career. This imposes several problems as the publication counts can be a quantitative indicator but fails to provide give valuable information regarding an author’s significance to their field or level of qualitative productivity.[8]
By disregarding the intricacy of the research work, funding sources, and other factors such as awards, patents, and academic honors from the research works, relying exclusively on publication count limits the researcher’s genuine influence.[9,10]
Therefore, it is crucial to consider how researchers safeguard their careers as academics, preserving the integrity of the scientific publication process and assuring the dissemination of excellent, well-conducted research.[11]
Researchers and academicians should be aware that while different metrics can be employed to summarize an author’s output and influence, each of them has advantages and disadvantages. No metric in itself can be perfect. Hence, the combination of metrics can comprehensively assess an author’s productivity and impact on his field.[12]
Therefore, this viewpoint highlights various cutting-edge metrics beyond just counting the number of articles an author has published to evaluate the author’s productivity and impact. This will add to the continuing debate about how to assess and acknowledge authors’ efforts in today’s quickly evolving academic environment.
Why is it necessary to evaluate author productivity and impact correctly?
There is an imperative need for a comprehensive assessment of the author’s impact, as it directly impacts his academic career, their affiliate institutions, and on community.[13]
These comprehensive evaluations assist funding agencies and institutions make informed decisions regarding job openings, promotions, grants, and other similar possibilities.[14]
A good assessment can help identify authors area of interest and possible gaps that need additional funds or resources, thereby making evidence-based decisions on research funds allocation. In addition, a thoughtful evaluation of the author’s influence might raise public confidence in scientific research.[15]
By unfairly comparing researchers based on the number of publications, erroneous or misleading appraisals could demotivate other researchers and ultimately impede scientific advancement.
Methods for evaluating author productivity and impact correctly
H-index: The H-index, also called the Hirsch index, introduced in 2005, is a metric used to assess the impact and productivity of an author’s research. This index is named after physicist Jorge Hirsch. It considers the number of publications the researcher had authored and the citation count each publication attained.[16]
The H-index is an author-level metric and shows the impact of an author. The H-index is simple to calculate, yet young researchers find it difficult to understand. First, to calculate the H-index, we need to sort all the publications of the author in descending order based on his/her citation counts. The H-index in simple terms means the author’s “nth” publication has received “n” number of citations. Taking an example, an author’s H-index of 9 indicates that he or she has at least nine publications, each of which has been cited at least nine times [Figure 1].[16]
Figure 1.
Demonstration of H-index calculation
Various online databases provide the H-index of authors for free by calculating their number of publications and the citation count associated with them. Such databases are Scopus, Web of Science, Google Scholar, and Vidwan.[16]
The H-index is commonly used nowadays in academic settings for various purposes such as evaluation of researchers or academicians for their impact in relevant fields, increase in tenure, promotions, and decision on funds for research.[16]
The main reason for its popularity is the ease of calculation and simplified comparison between researchers and academicians as well. In addition, it assesses the span of a researcher’s career by assessing his/her long-term impact in the field.
Despite its popularity, the H-index comes with criticism. One such problem is that it may have discrepancies in different databases. The main reason behind this is the variation in coverage and indexing policies. Some databases may also include coverages from conference papers, book chapters, and other scholarly outputs in their calculations; further inflating the H-index. In addition, there may be time lag for citations in different databases, thus aggravating the problem. Hence, care should be taken while considering the H-index for different purposes and from where the authors are quoting their H-index. If Author 1 quotes his/her H-index from Scopus and Author 2 from Google Scholar, they cannot be compared. They can only be compared when both Authors 1 and 2 quote from same databases. In simple terms, apples can be compared with apples and not oranges.[17,18]
There are other problems too, such as it does not consider the number of authors per publication and type of research, relevance to the community, contribution of individual authors in multi-author publications, and the quality of research. In addition, it is susceptible to manipulation through common tactics of self-citations and collaborative publications.[19]
It is worth noting that discrepancies in H-index calculations may arise among different platforms such as Web of Science, Scopus, and Google Scholar. Google Scholar uses an automatic indexing method and includes conference papers, book chapters, and other scholarly outputs in its calculations. It may yield inflated results compared to H-indexes generated by Web of Science and Scopus.[17,18]
i10 index: This index, a variant of the H-index, was introduced by Google Scholar in 2011 to measure the productivity of authors by counting the number of articles they have authored that have received at least 10 citations.[20]
The author’s i10 index will be 3 if they have 10 publications, of which three articles have each gotten 10 citations.[20]
Thus, the i10 index considers both the impact of an author in addition to output in terms of the number of publications. There are several advantages of the i10 index, such as its simplicity and clarity as compared to other indexes. In addition, it can help authors identify important papers from their publications.
The i10 index does have a few drawbacks. One of the fundamental shortcomings of Google Scholar is its inclusion of conference papers, book chapters, and other scholarly products in its computations. Consequently, the i10 index is not commonly utilized in other academic settings.[18]
Another shortcoming is, it does not consider the quality of the journals in which the articles were published or the author’s contribution in the case of collaborative research or research with multiple co-authors. Other drawbacks are that the i10 index becomes static after all publications have amassed 10 citations. Additionally, when determining impact, the i10 index does not consider papers with fewer than 10 citations.[18]
To enhance their number of publications and citations, some researchers may also manipulate their i10 index by dividing their work into smaller papers.
Field-Weighted Citation Impact (FWCI): It is the number of citations received by an author’s publications in comparison to the average or expected number of citations for similar publications in the same field and publication year. This is a metric to see how influential an author is as compared to others in the same field.[21]
For example, consider yourself in the race with another researcher. Whenever you receive a credit or a citation in their work, you get a point.
FWCI can be obtained from “SciVal,” which is a research analytics product from Elsevier, but it should be kept in mind that from 1996 onwards, SciVal only considers documents indexed in the Scopus database.
Now FWCI can be understood as, if FWCI is 1, it can be interpreted as having “world average impact.” If FWCI is more than 1, say 1.23, it can be interpreted as higher than expected citations based on the global average for similar publications and means that there were 23% more citations than predicted. An FWCI of less than 1 indicates that the work is less frequently acknowledged than predicted based on the global average.[21]
Although FWCI can be more appropriate to assess the authors’ impact in a particular field considering both the quantity and quality of publications by providing a more nuanced approach, it is not a perfect metric. It depends on the size and scope of the field, the availability of data on citation counts, and journal impact factors.
In addition, it is highly influenced by outlier publications with a larger number of citations in the portfolio of authors with less number of publications; one or two highly cited articles will skew the FWCI to a larger extent if the portfolio consists of only 10 articles in comparison to the portfolio of the author which consists of 200 articles. In simple terms, it is highly influenced by outliers.[22]
A similar metric is Category Normalized Citation Impact (CNCI), which is a research analytics product from Clarivate Analytics and can be obtained from “InCites.” The main problem is that both SciVal and InCites require a paid subscription to obtain the same.
Highly Cited Papers (HCP): Generally, the papers that outperform in terms of the number of citations received per year compared to other papers of the same field and considered in the top 1% are considered HCP. The metric can help rank authors according to the percentage of HCP. This can help identify authors who consistently produce high-impact work.[23]
This percentage can help judge the author’s impact and productivity as HCPs are the most impactful as compared to other papers of the same field and year.[24]
Author-level Eigenfactor Score (ALEF): Eigenfactor scores, which were earlier only calculated for ranking journals, departments, or institutions, can now also be calculated for individual authors known as ALEF.
This metric is based on the eigenfactor algorithm (EFA), which considers a weighted number of citations for citable publications. Thus, if a publication receives a citation from a prestigious journal, it will have more weightage as compared to those publications that receive citations from less prestigious journals. The main advantage of this algorithm is that it does not consider self-citations.[25]
West et al.[26] gave a detailed method of using this EFA to calculate ALEF, which takes into consideration different citation practices across different disciplines and helps assess the productivity and impact of an author in a novel manner.
Author Impact Factor (AIF): This metric is simply an extension of impact factor (IF) at the author level. This can be easily calculated by dividing the total number of citations that the author’s publications receive by the total number of publications he/she had authored in a given time frame.[27]
AIF has certain limitations and criticisms, it does not consider the field of study, publication type, and citation practices, which may yield a biased calculation; hence, it should be used in conjunction with other metrics to get the overall picture of the author’s impact in his/her field.
Altmetric Donut and Almetrics Attention Score (AAS): Simply said, Altmetric Donut and AAS show the amount and kind of attention an article has received after it has been published. It consists of two parts: the centre is an automatically generated alternative metric (AAS), or the weighted count of attention an article received, and an outer donut, the color of which depends on the kind of attention it received [Figure 2]. This is among one of the innovative methods for evaluating the influence of an author.[28]
Figure 2.
Demonstration of Altmetric Donut and Almetrics Attention Score (AAS)
Three factors determine AAS: volume, source, and authors. Volume is the total number of citations an article receives per person per source; the higher the AAS, the greater is the number of times the article is cited in different sources. Similarly, source indicates the source of citation and assigns weightage to it; a news article, for instance, will receive a higher weightage than a citation in a blog post. Finally, it removes subjective bias by taking into account the amount of mentions that the author gives to a particular article.[28]
Similarly, the color of the Altmetric Donut also holds a significant meaning [Figure 2].
Altmetric Donut and AAS can aid traditional metrics and are getting into the limelight due to the fact of capturing the impact of an author beyond traditional metrics, which only takes citations into account.
Journal Impact Factor (JIF): While JIF alone cannot accurately measure an author’s impact at the author level, it can be combined with other metrics to accomplish the same goal. Utilizing this metric to assess impact is primarily dependent on how it is calculated. IFs vary throughout fields and journals; the primary causes of these variations are types of research, differences in the behavior of citation across fields, and variations in the coverage by Journal Citation Reports (JCR).[29]
Another issue is that the impact of journals varies greatly depending on the scope of the field they cover. For example, reputable journals in a particular field, such as periodontology, tend to have a lesser impact factor than journals in general or multidisciplinary dentistry journals. In simple terms, journals that publish works in multidisciplinary fields tend to have higher IFs because they acquire citations from a wide range of researchers. Furthermore, we might argue that multidisciplinary journals are more appreciated because of a wide array of readership as opposed to specialty journals, which are only known by researchers in that field. This has an impact on citations to these journals and, consequently, their IFs.[29]
Co-authorship Network Analysis (CNA): CNA is a type of sociogram that shows researchers and their relationship with other co-researchers in terms of co-authorship in articles, grants, or any other professional connections.[30]
Each bigger central circle in CNA represents an individual author, and the publications of that author are represented by the smaller squares that surround the larger central circle. Author collaboration is indicated by the location of the node [Figure 3]. The CNA with highly interconnected nodes depicts that the author is more collaborative, more productive, and more likely to be cited.[30]
Figure 3.
Demonstration of Co-authorship Network Analysis (CNA) (Figure adapted from Fagan et al. 2018)[30]
Awards and honors: In line with the topic of interest, honors and awards bestowed upon an author by esteemed organizations in appreciation of his/her professional accomplishments might serve as a gauge for evaluating the author’s influence and contribution to the area.[31]
For instance, receiving honors and awards from society and specialty associations or winning a major prize such as the Nobel Prize.
These honors and prizes, however, are not a perfect gauge of an author’s influence or production because they may be tainted by subjective bias due to their whole reliance on the standards and selection procedures of the granting body.
Citation Percentile: A citation percentile measures an author’s published papers’ influence and impact compared to a benchmark set of comparable papers on the same subject or area of study. It essentially counts the number of citations a piece of work received compared to a benchmark group of articles comparable to it (in terms of subject, publication year, and document type). For instance, if the paper’s citation percentile is 20%, it has more citations than 20% of other articles on the same subject. Thus, it is used to assess the visibility and impact of researchers’ work in its field.[32]
N-Index: The N-Index is easy to calculate as it only considers the number of publications. The main disadvantage is that it promotes quantity over quality and can be manipulated by salami-slicing, unwanted co-authorships, etc., In addition, it offers no insight into influence about the impact on the field and community.
Author Contribution Index (ACI): ACI is based on the percentage of contributions made by individual author at the submission of manuscripts in collaborative research. As this takes into account the contributions of the authors as per The International Committee of Medical Journal Editors (ICMJE) criteria for authorship, it provides a more transparent approach to assessing an author’s productivity in a multi-author publication.[33]
For example, if we are considering contributions in the form of contributions, which can include conceptualization, methodology, data collection, analysis, writing, and editing, and we assign 10% for each contribution made, then:
If Author A contributes to conceptualization, methodology, and data collection, then his/her contribution will be 30%. If Author B contributes to conceptualization, methodology, data collection, analysis, writing, and editing, then his/her contribution will be 60%.
ACI can be fair and transparent by considering such individual contributions, which other metrics lack in collaborative research.
The summary of all the author metrics along with the advantages and disadvantages are shown in detail in Table 1.
Table 1.
Different author-level metrics with their advantage and disadvantages
| Metrics | Advantage | Disadvantage |
|---|---|---|
| H-index[16] | Ease of calculation. | Discrepancies on different databases based on coverage. |
| Widely accepted for academic purposes. | Time lag for citations in different databases. | |
| Easy manipulation through self-citations and collaborative publications. | ||
| i10 index[18,20] | Ease of calculation. Simple and straightforward as compared to other indexes. | Does not consider the quality of the journals in which the article is published. |
| Becomes static after all publications have amassed ten citations. Inability to determine the impact of the articles that have fewer than ten citations. | ||
| Easy to manipulate by self-citations and by dividing their work into smaller papers. | ||
| Not commonly utilized in other academic settings. | ||
| Field Weighted Citation Impact (FWCI)[21] | It shows how influential an author is as compared to others in the same field. | Highly influenced by outlier publications with a larger number of citations. |
| Depends on the size and scope of the field. | ||
| Depends on the availability of data on citation counts and journal impact factors. | ||
| Highly Cited Papers (HCP)[23,24] | Shows the most influential or impactful publications within their respective fields. | Highly dependent on citation practices of different fields. |
| Acts as benchmark for researchers and institutions. | May be susceptible to publication bias as publications in high-impact journals are more likely to become highly cited and thus become HCP. | |
| Author-level Eigen Factor | It does not consider self-citations. | Complex calculation. |
| Score (ALEF)[25,26] | Shows field variability and requires extensive data on citations and significant resources to compute it. | |
| Over-reliance on quantitative measures ignoring quality. | ||
| Author Impact Factor (AIF)[27] | It provides a more comprehensive assessment by considering all the citations the publication had received. | Do not consider the field of study, publication type, and citation practices. |
| By normalization, it can be used for more efficient comparisons of authors in different fields. | It needs to be normalized for efficient comparisons of authors in different fields. | |
| Altmetric Donut, and Almetrics Attention Score (AAS)[28] | Visual representation for quick and easy interpretation. Comprehensive coverage depicts the impact of an author beyond traditional metrics which take into account only citations, such as media coverage, blogs, public policy documents, Twitter, and online forums. | Visual depiction of the author’s impact by various metrics in the form of donuts might lead to over-simplification or misinterpretation of the data for those who are not familiar. Source bias in the form of overemphasis on the popularity of media and other sources over scientific rigor may lead to biased outcomes. |
| Journal impact factor (JIF)[29] | Widely recognized metric for quality of journals. | Represents the quality of the journal and its citations but is not a true indicator of an author’s productivity. |
| Impact factors vary throughout fields and journals because of differences in the behavior of citations across fields, and variations in the coverage by Journal Citation Reports (JCR). | ||
| Depends on the scope of the field they cover | ||
| Co-authorship Network Analysis (CNA)[30] | Visual map representation is more intuitive and informative; thus, it is easier to communicate collaboration networks and authors’ productivity among different fields. Gives in-depth insights into authors’ collaborative practices. Highlights interdisciplinary works and shows the connection of authors with different fields. | Complex analysis. Biased toward bigger teams with more collaborative networks may appear more productive as compared to smaller research teams with smaller collaboration networks. More resource-intensive to compute. |
| Citation Percentile[32] | Easy comparison of productivity across disciplines. It can account for differences in citation practices in different fields. It can also help identify highly cited papers more efficiently. | Not widely used. May be a disadvantage for young researchers as it tends to favor established researchers with a long citation history. |
| N Index | Easy to calculate. | Promotes quantity over quality. |
| Only considers the number of publications. | Easy to manipulate by salami-slicing, unwanted co-authorships, etc. | |
| No insight into the influence of the impact on the field and community. | ||
| Author Contribution Index (ACI)[33] | Easy to calculate. | Easy to manipulate by ghost authorship and citation swapping. |
| Considers individual author contributions as per ICMJE to calculate the percentage of author contributions. |
Conclusion
In the “publish or perish” era, the traditional metric of publication counts falls short of evaluating an author’s productivity and impact. To maintain the credibility of scientific research, it is necessary to reform the publication metrics and explore innovative ways of evaluating an author’s contributions. Accurately assessing an author’s productivity and impact has significant implications on their academic career, institution, and the broader scientific community. Through this viewpoint, we have described multiple metrics such as the H-index, i10 index, FWCI, HCP, ALEF, AIF, AAS, JIF, CNA, awards/honors, citation percentile, n-index, and ACI; through which one can determine the true impact and productivity of an author. Other measures such as awards and honors, research collaborations, research output diversity, and JIF can further serve the purpose. This can also help funding agencies make informed decisions, improve resource allocation, and enhance public trust in scientific research. It is crucial to address these issues and continue the ongoing discussion on best method to evaluate and recognize the contributions of authors in today’s rapidly changing academic landscape.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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