Conducting Literature Reviews Hermeneutically
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it