Advocating beyond the academy: dilemmas of communicating relevant research results
Bibliographic record
Abstract
Drawing from experiences in Northern Indigenous Canada, Uganda, and Vietnam, we discuss the challenges encountered while trying to communicate relevant results to local communities with whom we work. Wavering between participatory and advocacy research, we explore how we grapple with finding the right audience with whom to share results, our attempts to craft communication to be relevant within specific contexts, and dilemmas over self-censorship. We also document our struggles to manage our own expectations and those of the communities with whom we work regarding the ability of our research to broker change. This article emerged from our frustration at wanting to be accountable to our interviewee communities, but finding few academic articles that go beyond ideals to examine how researchers often struggle to meet these expectations. While participatory approaches are increasingly mainstreamed in social science work, we argue that advocacy research can be a more appropriate response to community needs in certain cases.
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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 | 0.415 | 0.446 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.055 | 0.114 |
| Scholarly communication | 0.045 | 0.035 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.016 | 0.021 |
| 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, 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".