The Impact of Citation Timing: A Framework and Examples
Bibliographic record
Abstract
The literature on research evaluation has noted important differences in citation time patterns between disciplines, high and low ranked journals and types of publications. Delays in the receipt of citations suggest that the diffusion of knowledge following discovery is slower and given the passage of time the research contribution may be less valuable. This paper provides a framework for the comparison of different citation time patterns. Using principles drawn from the literature on stochastic dominance we show that comparisons of time patterns can be based on very general characteristics of cost of delay functions. When a particular function is used to represent the cost of delay, the magnitude of the impact of differences in citation time patterns can be assessed using simple exponential discounting. We demonstrate the application of this framework in assessing different citation time patterns by applying it to comparisons of 10-year citation records for: leading journals in economics, different business subject areas, journals in economics compared with those in neuroscience and the research output of individual economists.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".