Dialectics as a tool for navigating the mandates of scholarship and community impact: learning from reflexive practices
Bibliographic record
Abstract
Universities are increasingly demanding that faculty fulfill multiple mandates including producing scholarly published work and demonstrating community impact. As such, faculty experience the challenges of community involvement and impact, while also ensuring that community involvement leads to published research. This manuscript discusses a participatory action research (PAR) project that failed to achieve its initial outcome goal of community impact. Heeding the advice of previous scholars, we attempt to ‘work the ruins’ of failure, to stimulate discussion that may assist other researchers in efforts to fulfill this dual mandate of publication and community impact. Rooted in dialectics, and based on our own reflexive practice, and interviews with program staff and participants, we suggest three dialectics for consideration for future scholarship: 1) Community led AND researcher led; 2) Failure AND success; and 3) Messy AND publishable.
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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 | 0.162 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.017 | 0.123 |
| Scholarly communication | 0.030 | 0.031 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".