Characterising socially accountable research: a scoping review protocol paper
Bibliographic record
Abstract
INTRODUCTION: Social accountability is a key value and aspirational goal of many medical institutions. While much has been studied on social accountability in the context of medical education and institutions, less research has examined how social accountability influences research. In light of this absence, the objective of our scoping review is to research the following questions: (1) What characterises socially accountable research (SAR), and how is it expressed and experienced? (2) How do language, positionality, and worldview influence SAR?, and (3) What structures and considerations are necessary to support successful SAR in local and global contexts? METHODS AND ANALYSIS: , Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and Joanna Briggs Institute (JBI) guidelines will be followed. The search strategy was adapted and applied to MEDLINE, Embase, ERIC, and CINAHL databases. A total of n=5289 eligible articles were identified. Articles were excluded if they were published before 1995, were in a language other than English, or were duplicates, leaving n=2840 articles for title/abstract screening. ETHICS AND DISSEMINATION: Ethical approval is not required to complete this study. We will take an integrated knowledge translation approach. Throughout the project, results will be disseminated to knowledge users (ie, consultations, following Arksey and O'Malley). Our findings will be presented to the larger academic community, policymakers, and healthcare practitioners through presentations, reports, newsletters, and an online repository. TRIAL REGISTRATION NUMBER: Open Science Framework 16 July 2024. osf.io/mvhnu.
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 | 0.270 | 0.267 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".