Is It Us or Them? The Challenge of Getting Journalists to Participate in Academic Research
Bibliographic record
Abstract
For journalism research rooted in a sociological framework, gaining access to journalists is crucial for data collection. However, journalists are documentably difficult to recruit as participants, frequently due to limited time and resources in shrinking newsrooms, sometimes because of roadblocks put up by leery newsroom management. As part of the data collection process for the Canadian branch of the international Journalistic Role Performance project, researchers tracked issues raised by journalists that may have impacted survey recruitment and completion. These issues were frequently grounded in the context of survey questions designed to be operationalizable in vastly different cultures, and were also documented by other researchers in both the Global North and South who were cooperating in this comparative study. Despite first refusing or completely refusing survey participation, though, some Canadian journalists were willing to be interviewed, giving researchers the opportunity to explore how to best engage journalists in the research process, and design research tools to be shared in different media systems. Using a lens of social exchange, this paper provides unique insight, from the perspective of researchers and participants, on the importance of relationship-building as a focal point in order to adequately contextualize findings within a sociology of news framework.
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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 | 0.354 | 0.476 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.041 |
| Scholarly communication | 0.041 | 0.040 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".