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Programmers, Professors, and Parasites: Credit and Co-Authorship in Computer Science

Science and Engineering Ethics volume 15, pages 467–489 (2009)Cite this article

Abstract

This article presents an in-depth analysis of past and present publishing practices in academic computer science to suggest the establishment of a more consistent publishing standard. Historical precedent for academic publishing in computer science is established through the study of anecdotes as well as statistics collected from databases of published computer science papers. After examining these facts alongside information about analogous publishing situations and standards in other scientific fields, the article concludes with a list of basic principles that should be adopted in any computer science publishing standard. These principles would contribute to the reliability and scientific nature of academic publications in computer science and would allow for more straightforward discourse in future publications.

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Notes

  1. Analyses of the DBLP and NRC Research-Doctorate Programs in the United States data were carried out by the author. Programs in C++ were devised for parsing and analyzing the data; for example, the NRC data analysis program is shown in the Appendix. Figures 2–6 were produced using the output of these programs, exported to a spreadsheet application. Occasionally it was not possible to parse the data correctly (due to incorrect formatting or other inconsistencies); these situations were documented (see the Appendix for an example). Instances of this problem were relatively small and should not affect the trends observed in this study.

  2. Personal communication.

  3. The R 2 value is the “coefficient of determination” for a statistical fit line. R 2 values close to 1 represent ideal fit lines, while R 2 ≈ 0 implies little to no correlation between a fit curve and the data. To produce these values, optimal least-squared fit curves were chosen from standard models (exponential, logarithmic, linear) for statistical variation. The curves are imposed on Figs. 5 and 6 for inspection. Here we see that primary authorship and computer science “scholarly quality” are related by a fit line with sufficiently high R 2 value to indicate some type of correlation, while the relationship between secondary authorship and “scholarly quality” is insubstantial.

  4. Personal communication.

  5. Incidentally, the NRC data concerning primary versus secondary authorship apparently is somewhat flawed. Certain schools with considerable numbers of publications are identified as having no secondary-authored publications, which is unlikely given that any team with multiple researchers from the same school would have to have one or more secondary authors since only one person can appear first on an author list. Furthermore, not all lines in the data file have the right number of characters to agree with the description of the data format.

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Acknowledgements

Special thanks to James Robert Wood, Stanford Department of English and Program in Writing and Rhetoric, for his advice throughout the writing process and revisions of the final paper. Additional thanks to Prof. Clifford Nass (Stanford Department of Communications), Prof. Andrea Lunsford (Stanford Department of English), and my family for their support.

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Affiliations

  1. Department of Computer Science, Stanford University, PO Box 13092, Stanford, CA, 94309, USA

    Justin Solomon

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Correspondence to Justin Solomon.

Appendix: C++ Code Used to Parse NRC Study Data

Appendix: C++ Code Used to Parse NRC Study Data

The following short program was used to parse the data accompanying the National Research Council’s study, Research-Doctorate Programs in the United States. It is included to make transparent the methods used for data analysis and to enable easier analysis of the NRC data in future studies.Footnote 5

  • #include <iostream>

  • #include <fstream>

  • #include <vector>

  • #include <string>

  • #include <conio.h>

  • #include <set>

  • using namespace std;

  • int main() {

  •  //Citation data

  •  ifstream infile(“PUB_CIT.dat”,ios::binary|ios::in);

  •  //List of faculty

  •  ifstream faclist(“FACLIST.dat”,ios::binary|ios::in);

  •  //Output file

  •  ofstream outfile(“pub_cit_analysis2.txt”);

  •  set<string> validnames;//set of names of CS researchers

  •  string curline;

  •  int b = 0, cs=0;//b = number of invalid lines; cs = number of CS professors

  •  while (getline(faclist,curline), !faclist.fail()) {

  •   //according to NRC standard, all lines in FACLIST.dat should have 63 characters

  •   if (curline.length() < 63) {

  •    b++;//invalid line

  •    continue;

  •   }

  •   string facname = curline.substr(0,5);//Name of faculty; NRC 5-character code

  •   string progcode = curline.substr(60,2);//Program/department code

  •   if (progcode == “26”) {//code 26 = computer science

  •    validnames.insert(facname);

  •    cs++;

  •   }

  •  }

  •  cout ≪ b ≪ “bad lines.\n”;

  •  cout ≪ cs ≪ “CS profs.\n”;

  •  //all schools indexed by 3-digit code, so vectors have 1000 elements to cover all codes

  •  vector<int> primary_authorship(1000);//number of primary-authored papers per school

  •  vector<int> secondary_authorship(1000);//number of secondary-authored papers per school

  •  vector<int> single(1000);//number of singly-authored papers per school

  •  vector<int> multiple(1000);//number of multiple-authored papers per school

  •  vector<int> total(1000);//total number of papers per school

  •  string empty=““;

  •  int numlines = 0;//number of lines parsed

  •  int numbad = 0;//number of bad lines

  •  while (getline(infile,curline), !infile.fail()) {//for each publication

  •   numlines++;//update status

  •   if (curline.length() < 98) {//invalid line

  •    numbad++;

  •    continue;

  •   }

  • if (!validnames.count(curline.substr(0,5)))//not a CS publication

  •    continue;

  •   char t = curline[2];

  •   if (t == ‘A’ || t == ‘O’ || t == ‘S’ || t == ‘Y’)

  •    continue;//only accept proceedings, journals

  •   int numAuthors = curline[19]-’0’;//number of authors (between 0 and 9)

  •   if (numAuthors == 1) single[c]++;

  •   else if (numAuthors > 1) multiple[c]++;

  •   if (curline[31] == ‘P’) primary_authorship[c]++;//‘P’ indicates primary authorship

  •   else if (curline[31] == ‘S’) secondary_authorship[c]++;

  •   total[c]++;

  •  }

  •  for (int i = 0; i < 1000; i++)

  •   if (total[i]) {//if school published in CS, output data

  •    outfile ≪ i ≪ ‘‘;

  •    outfile ≪ total[i] ≪ ‘‘;

  •    outfile ≪ single[i] ≪ ‘‘≪ multiple[i] ≪ ‘‘;

  •    outfile ≪ primary_authorship[i] ≪ ‘‘≪ secondary_authorship[i] ≪ ‘‘;

  •    outfile ≪ (double)primary_authorship[i]/(primary_authorship[i] +

  •     secondary_authorship[i]);

  •    outfile ≪ endl;

  •   }

  •  cout ≪ “Data processing is done.\n”;

  •  cout ≪ numlines ≪ “lines processed.\n”;

  •  cout ≪ numbad ≪ “bad lines.\n”;

  •  getch();

  • }

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Solomon, J. Programmers, Professors, and Parasites: Credit and Co-Authorship in Computer Science. Sci Eng Ethics 15, 467–489 (2009). https://doi.org/10.1007/s11948-009-9119-4

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Keywords

  • Co-authorship
  • Computer science research
  • Publishing