Who Are We Citing and How? A SoTL Citation Analysis
Bibliographic record
Abstract
The Scholarship of Teaching and Learning (SoTL) is continuing to develop as a multidisciplinary, international field of practice and a topic of study itself. As the field matures, one area of interest has been the SoTL literature review. However, there has not been an evidence-based study of SoTL citation practices. The purpose of this study was to analyze one year’s worth of articles from this journal to see how references and in-text citations are used. Overall, 514 references and 954 in-text citations were found across 18 articles. A diverse range of multidisciplinary and specialized academic journals were cited; 8 percent of in-text citations cited a source other than an academic journal. Each reference and in-text citation was coded as either substantive (Applied, Contrastive, or Supportive) or non-substantive (Reviewed or Perfunctory). A high rate of in-text citations (74 percent) were found to be non-substantive, with the majority of non-substantive in-text citations (71 percent) found in either the Introduction or Literature Review sections of the articles. Conversely, of the 26 percent of in-text citations considered substantive, 50 percent were found in either the Results & Discussion or Conclusion sections. We demonstrate the use of the coding scheme as a self-assessment tool and conclude by suggesting that SoTL authors and reviewers could use it to assess the depth and breadth of their literature reviews.
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
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 | 0.046 | 0.219 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.068 | 0.068 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".