What Does it Take for a Canadian Political Scientist to be Cited?<sup>*</sup>
Bibliographic record
Abstract
Objectives. The article examines the factors that influence the frequency whereby scholarly articles published by Canadian political scientists are cited. Method. We collected data on 1,860 journal articles published between 1985 and 2005 by 758 Canadian political scientists and listed in the Social Science Citation Index. Using these data, we performed OLS and tobit estimations to identify factors influencing citation frequency. Results. The regressions show that the reputation of the journal in which the article is published, though important, does not explain everything. The gender of the author(s), the number of authors, the geographical focus of the article, the field, and the methodology also matter. Conclusion. An article is more likely to be widely cited if it is published in a prestigious journal, if it is written by several authors, if it applies quantitative methods, if it compares countries, and if it deals with administration and public policy or elections and political parties. Faculty members who belong to larger departments and those who are women are more cited.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".