Bibliographic record
Abstract
This workshop provides a high-level overview of the process for preparing an integrative literature review. An “integrative literature review is a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated” (Torraco, 2005, p. 356) This workshop first explains why integrative literature reviews are becoming increasingly popular in research circles, then contrasts integrative literature reviews with meta-analyses, meta-syntheses and other related forms of advanced literature reviews, as well as with more traditional literature reviews. Next, this workshop describes methodological considerations for finding, including, and excluding studies; processes for reviewing and classifying the literature, analyzing the resulting data, and the four types of findings that typical integrative literature reviews typically report. The workshop closes by directing participants to samples of integrative literature reviews and identifying considerations for submitting these reviews to peer-reviewed publications. To guide participants through this experience, this workshop is built around a sample literature review project. Participants will practice the skills taught by applying them to the sample project. For example, to illustrate methodological considerations, participants will identify characteristics for including and excluding studies in a search and, later, will receive a sample list of studies to determine whether or not to actually include them in the review.
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 | 0.139 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".