Bibliographic record
Abstract
But their accounts foreground another problem in our field that is, perhaps, even more significant.My experience as a reader often leaves me wondering why work in philosophy of education is not cited by other philosophers of education working in similar areas. 2 As Hayden's (2012) important empirical work demonstrates, philosophers of education work on different topics but there are certainly clusters of interest present, which would suggest that there are grounds for greater citation within our own field.Drawing on my own experience as a reader and thinking more about the results of Hayden's project, I began looking informally at patterns of citation in individual articles.This brief and nonsystematic initial look at literature in philosophy of education journals validated my concern that we might not be citing each other enough, and it led me to the current project: an empirical examination of citation patterns in philosophy of education journals.The goal of this brief paper is to explore in an empirical manner how our field engages with the literature we publish.In this paper, I present a citation analysis of three prominent journals of philosophy of education: Studies in Philosophy of Education, Journal of Philosophy of Education, and Educational Theory.By exploring patterns of citation and self-citation in our field, I hope to spark a conversation about what citation patterns say about our field and its future.As will become clear from these results, it may be hard to make the case to scholars outside of our field that our work is important when it doesn't seem as though scholars within the field engage with the work particularly well.
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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 | 0.014 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.124 | 0.274 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".