Patterns of citations for the growth of knowledge: a Foucauldian perspective
Bibliographic record
Abstract
Purpose The purpose of this paper is to sensitize researchers to qualitative citation patterns that characterize original research, contribute toward the growth of knowledge and, ultimately, promote scientific progress. Design/methodology/approach This study describes how ideas are intertextually inserted into citing works to create new concepts and theories, thereby contributing to the growth of knowledge. By combining existing perspectives and dimensions of citations with Foucauldian theory, this study develops a typology of qualitative citation patterns for the growth of knowledge and uses examples from two classic works to illustrate how these citation patterns can be identified and applied. Findings A clearer understanding of the motivations behind citations becomes possible by focusing on the qualitative patterns of citations rather than on their quantitative features. The proposed typology includes the following patterns: original, conceptual, organic, juxtapositional, peripheral, persuasive, acknowledgment, perfunctory, inconsistent and plagiaristic. Originality/value In contrast to quantitative evaluations of the role and value of citations, this study focuses on the qualitative characteristics of citations, in the form of specific patterns of citations that engender original and novel research and those that may not. By integrating Foucauldian analysis of discourse with existing theories of citations, this study offers a more nuanced and refined typology of citations that can be used by researchers to gain a deeper semantic understanding of citations.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".