Reinvigorating the Notion of Becoming “Scholars Before Researchers”: Experiences With Systematic Literature Reviews
Bibliographic record
Abstract
Systematic literature reviews (SLRs) present scholars with an opportunity to develop deep conceptual and methodological understanding of their field of interest. Unfortunately, this skill is not necessarily a compulsory component of one’s formal graduate training. In this paper, we have storied our lived experiences with SLRs in the form of 13 vignettes. These vignettes were created from a series of open-ended questions that were developed from continued engagement with 10 articles, which were foundational in our own teaching and learning of the SLR method. A content analysis of the vignettes revealed five inter-related themes: (1) Rigor, (2) Process, (3) Benefits, (4) Learning, and (5) Teaching. Each of these five themes are discussed in relation to recollections of our experiences with SLRs. We provide recommendations for engagement with the SLR method with the goal of empowering researchers, professors, and students to explore its use in postsecondary coursework and research activities.
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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 | 0.507 | 0.556 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.021 | 0.049 |
| Scholarly communication | 0.034 | 0.045 |
| Open science | 0.007 | 0.049 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".