The power–law relationship between citation‐based performance and collaboration in articles in management journals: A scale‐independent approach
Bibliographic record
Abstract
The objective of this article is to determine if academic collaboration is associated with the citation‐based performance of articles that are published in management journals. We analyzed 127,812 articles published between 1988 and 2013 in 173 journals on the ISI Web of Science in the “management” category. Collaboration occurred in approximately 60% of all articles. A power–law relationship was found between citation‐based performance and journal size and collaboration patterns. The number of citations expected by collaborative articles increases 21.89 or 3.7 times when the number of collaborative articles published in a journal doubles. The number of citations expected by noncollaborative articles only increases 21.35 or 2.55 times if a journal publishes double the number of noncollaborative articles. The Matthew effect is stronger for collaborative than for noncollaborative articles. Scale‐independent indicators increase the confidence in the evaluation of the impact of the articles published in management journals.
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
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 | MetaresearchBibliometrics Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.026 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".