A scoping review of co-production between researchers and journalists in research communication
Bibliographic record
Abstract
Co-production is rapidly gaining purchase as an approach to making research matter more to diverse audiences. There exists a wealth of information about co-production in areas such as public administration and sustainability science, but comparatively little within the specific area of research communication. In particular, little is known about the harnessing the potential of researchers and journalists engaging in co-production to generate evidence-based knowledge, foster an informed public, and achieve societal impacts. This review aimed to address that gap in the knowledge base by systematically mapping the theoretical and empirical literature related to co-production between researchers and journalists in research communication. Given the paucity of study in this area, we advanced this aim by synthesizing the extant literature that has explored the more general concept of interactions between researchers and journalists. Following a scoping review methodology, a total of 60 articles were selected for inclusion in this review. We analyzed the included articles following a systematic method of using a data extraction framework to synthesize and interpret contextual (country of the study or author [s], publication type, sector, and methods) and thematic (objectives, theoretical framework, findings) information. Three cross-cutting themes were identified that help to elucidate important considerations for researchers and journalists engaged in or considering engaging in co-production in research communication: (a) the roles of researchers and journalists; (b) the pitfalls and promises of co-production; and (c) the barriers and facilitators of co-production. Following an in-depth examination of these themes, we conclude with a synopsis of the literature along with identifying two major topics for progressing current knowledge and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communication Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchScholarly communication Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.100 | 0.279 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.039 | 0.043 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".