Bibliographic record
Abstract
The number of “citations” generated by an investigator, article, or journal is increasingly used as a measure of impact of the investigator, article, or journal on a research topic and as a benchmark of research quality (Monastersky 2005, Greger 2006, Lund 2006). Many investigators consider the citation record of journals when they select where to submit their research manuscripts for publication. Journals are increasing soliciting review articles because they are cited more often and raise the journals’ citation indexes. The current National Research Council (NRC) Survey of Research Doctorate Programs (which will assess all doctoral programs in food science and nutrition as well as those in liberal arts and sciences and engineering) will use citations as an index to measure the impact of research in doctoral programs (Board on Higher Education and Workforce, National Research Council 2006). Increasingly citation indexes are used during merit evaluations of faculty members and departments by universities. These “citations” are based on the large databases (sometimes called the ISI Web of Knowledge) maintained by Thomson Scientific (2006). One of the subsets of this system is called ISIHighly Cited.comSM, which identifies the researchers whose total publications received the most citations in their fields during a twenty-year period (1981–1999) (Thomson ISI 2006). The methodology by which Thomson Scientific identifies the most citations is complicated and has limitations; thus an explanation of the methodology is included. Thomson Scientific (2006) uses computer algorithms to track the citations of all articles published in thousands of science (biological, physical, and social) journals annually. About 27 million citations were reviewed for the database in 2004 alone (Monastersky 2005). To create the ISI List of Highly Cited Investigators, Thomson Scientific identified 21 research categories in their citation databases for 1981–99: Agricultural Sciences, Biology & Biochemistry, Chemistry, Clinical Medicine, Computer Science, Ecology/Environment, Economics & Business, Engineering, Geosciences, Immunology, Material Science, Mathematics, Microbiology, Molecular Biology & Genetics, Neuroscience, Pharmacology, Physics, Plant & Animal Science, Psychology/Psychiatry, Social Sciences, general, and Space Sciences (Thomson Scientific 2006). Journals were classified as to their content into one or more these 21 categories. About 200 journals were indexed for the Agricultural Sciences category, including the Journal of Food Science and Food Technology. These food science journals were not included in any of the other categories besides Agricultural Sciences. A few “multidisciplinary” journals (for example, Nature, Proceeding of the National Academy of Science, and Science) were subject to more detailed analyses in which articles were assigned individually to one of the 21 categories. A record was created for every author of each indexed article. Each author of an article got credit for all the citations of the article. Citations were then summed by authors' names. Then investigators were assigned to categories in ISI's Highly Cited database based on the ISI categories assigned to the articles that they published. Self-citations were not considered. The most cited (about 250) researchers in each of the 21 categories were identified. Thomson ISI (2006) identified 5459 investigators as “highly cited” and classified 251 of these investigators as working in the Agricultural Sciences. Forty members of the Institute of Food Technologists (IFT) were identified as “highly cited” investigators and all were classified as working in the Agricultural Sciences (Table 1). An additional 13 “highly cited” investigators (who were not members of IFT) published in the Journal of Food Science and/or Food Technology. In comparison, ISI identified 29 members of the American Society of Nutrition (ASN) as “highly cited.” However, an additional 91 “highly cited” investigators published in the Journal of Nutrition and/or American Journal of Clinical Nutrition (publications of ASN). Thirteen “highly cited” investigators were members of both IFT and ASN. Three (7.5%) of the “highly cited” IFT members worked in Canada; the rest worked in the United States. However, 6% and 42% of all the “highly cited” investigators in Agricultural Sciences worked in Canada and the United States, respectively. Although being included in the list of “highly cited” investigators by ISI Scientific can be considered an honor, the lists reflect several other factors. The number of citations necessary to be included as a “highly cited” investigator varied among categories. The number of citations necessary to be “highly cited” in Agricultural Sciences was less than for those in Biology & Biochemistry. The current list of “highly cited investigators” has not been updated since 2005 and considers only citations from 1981–1999. Highly cited investigators are often clustered in areas that are receiving large amounts of federal research funding, which generally is not true for food science. It is easy to criticize citation indexes as just “data mining” tools but these indexes are increasingly being used as a metric to judge the impact/quality of journals, graduate programs, departments, and individual researchers. Food scientists can use these citation indexes to their advantage if they understand the indexes. Moreover, Thomson ISI demonstrated the importance of food science as a research field when they identified 40 IFT members as “highly cited” investigators.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.210 | 0.484 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.336 | 0.532 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.017 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".