Evaluating community-based research: Hearing the views of student research partners
Bibliographic record
Abstract
Despite the increasing popularity in the academy of collaborative approaches to research, evaluating the impacts of Students-as-Partners (SaP) initiatives has thus far received less systematic attention. This paper presents an evaluation of a participatory community-based research project where academics partnered with 15 mature students in a socio-economically disadvantaged estate in the south of Ireland to co-construct a household survey and conduct field research to gather the views of fellow residents on the regeneration of their area. The paper reports the findings of a subsequent qualitative, participatory evaluation of the student’s experience of this partnership with academics and its impacts. The findings illuminate some of the benefits and challenges of community-based staff-student research partnerships and points to the imperatives of aligning institutional, funder, and community participants’ capacities and objectives throughout the research cycle and the importance of evaluation to inform good practice in community-based research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.353 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".