Knowledge Synthesis in Engineering: A Practical Guide to Contextualizing Different Review Methodologies
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.
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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 | 0.299 | 0.389 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".