A classification of author co‐citations: Definitions and search strategies
Bibliographic record
Abstract
Abstract The term author co‐citation is defined and classified according to four distinct forms: the pure first‐author co‐citation , the pure author co‐citation , the general author co‐citation , and the special co‐author/co‐citation . Each form can be used to obtain one count in an author co‐citation study, based on a binary counting rule, which either recognizes the co‐citedness of two authors in a given reference list (1) or does not (0). Most studies using author co‐citations have relied solely on first‐author co‐citation counts as evidence of an author's oeuvre or body of work contributed to a research field. In this article, we argue that an author's contribution to a selected field of study should not be limited, but should be based on his/her complete list of publications, regardless of author ranking. We discuss the implications associated with using each co‐citation form and show where simple first‐author co‐citations fit within our classification scheme. Examples are given to substantiate each author co‐citation form defined in our classification, including a set of sample Dialog™ searches using references extracted from the SciSearch database.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsMetaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Bibliometrics Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.024 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.136 | 0.137 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".