Conducting Literature Reviews Hermeneutically
Bibliographic record
Abstract
It is well understood that conducting high quality literature reviews provides an important and solid foundation for research studies. While there is an abundance of resources available about how to conduct literature reviews for quantitative research, there are fewer publications available about how to conduct literature reviews for qualitative research, particularly research that is guided by hermeneutic philosophy. Rather than detailing how to conduct a hermeneutic literature review, in this paper we make the subtle, yet necessary, distinction that literature reviews included in research studies that are guided by hermeneutics should be conducted hermeneutically. We begin by reviewing the few resources that are currently available about conducting literature reviews for hermeneutic research and detail three different literature review processes for three hermeneutic studies. We then discuss how researchers, who are using hermeneutics to guide their research, might determine what literature should be included in their literature reviews. We close the paper by addressing the significance of rigour in literature reviews that are conducted hermeneutically.
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 | 0.514 | 0.709 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.055 | 0.034 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".