Knowledge Synthesis in Engineering: A Practical Guide to Contextualizing Different Review Methodologies
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
There is a rapidly increasing amount of scientific information being produced daily, and researchers have acknowledged the significant issues with being able to stay on top of new and emerging research. Literature reviews serve several purposes to combat this: 1) to synthesize research, 2) to critically evaluate it and 3) to provide recommendations. Evidence based systematic searching was initially developed in the medical field, grounded in the knowledge that while there was an importance to having an expert opinion, the best medical advice was based on the accumulation of results from multiple experiments. Engineering has long been borrowing from the review methodology, but this has been happening on a one-off basis, with little to no formal structure to the adaptations. Working with a cross disciplinary team of engineering and health librarians, at institutions across Canada, this paper seeks to contextualize knowledge synthesis for non-health librarians, provide fundamental information on engineering and health databases for reproducible searching, their capabilities and limitations, and open a conversation around working toward a rigorous new methodology applicable in cross disciplinary engineering contexts.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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