Claiming Space for Square Pegs: Community-Engaged Communication Scholarship and Faculty Assessment Policies
Bibliographic record
Abstract
When communication and media scholars work shoulder-to-shoulder with communities, the research products are necessarily as dynamic, creative, and diverse as the community members involved. Although such active scholarship generates rich, socially impactful knowledge, it often holds scant value within the arcane world of faculty tenure and promotion committees, where single-authored academic journal articles are the bread and butter of academic careers. As a result, members of the public are left to work with university partners who are typically precariously employed, with little institutional backing for community collaborations. Drawing on the Community-Engaged Scholarship Partnership’s research into Canadian faculty assessment policies, this article will lay out the case for concrete academic reforms that recognize, respect, and professionally support the “square pegs” of community-engaged media research.
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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 | 0.147 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.054 | 0.099 |
| Scholarly communication | 0.044 | 0.031 |
| Open science | 0.008 | 0.049 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 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".