Conducting a Systematic Literature Review in Education: A Basic Approach for Graduate Students
Bibliographic record
Abstract
Though essential for graduate students’ success, academic writing remains complex for a variety of reasons. Lack of institutional support and non-transparent writing practices leave graduate students in education to depend on the support of their academic supervisors. The aim of this paper is to familiarize graduate students with the genre of systematic literature review (SLR), as it is conducted in the field of education, by providing them with a self-paced approach to writing a SLR. This approach contains goals, explanations, and recommended time frames, while at the same time suggesting deliverables to be produced that would facilitate the writing of this important part of their research project.
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 | 0.461 | 0.469 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".