Interrogating Course-Related Public Interest Internships in Communications
Bibliographic record
Abstract
This article examines the benefits and drawbacks of for-credit, unpaid internships geared towards the public good. Attention is focused specifically on communication internships with non- governmental, non-profit, and community-based organizations. Drawing on a series of semi-structured interviews with students, staff, faculty, and host organizations, the author advances a critical model of service learning that more fully recognizes the labour of community partners and encourages students to consider what role they can and should play in advancing the public good. The article also highlights two key issues vis-à-vis public interest internships that are of particular relevance to the field of communications. The first is a disconnect between, on the one hand, communications as a theoretical field of study and, on the other hand, the skills communication students are typically expected to bring with them into their placements. The second is a growing tension between what different members of the university community expect out of public interest internships: politically safe forms of community engagement palatable to university administrations versus more activist-oriented placements with organizations and movements that contest structures of control both on and off campus. The author contends that communication programs must critically reflect upon how politically benign and/or contentious internships support their pedagogical goals and what resources need to be in place to meet the- se objectives.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".