Coming of Age: How JAC is Reflecting a National Research Agenda for Communications in Agriculture, Natural Resources, and Life Human Sciences
Bibliographic record
Abstract
This study analyzed communications research trends, topics, needs, and opportunities involving agriculture, natural resources, and life and human sciences since the development of a national research agenda (NRA) in 2007. A content analysis of 23 issues of the Journal of Applied Communications (JAC) published over 7.5 years (2008 to mid-2015) examined the degree to which the articles reflected the priority research areas (PRAs), key research questions (KRQs), and priority initiatives (PIs) identified in the NRA. Findings showed a watershed period from 2011-2014 in which the journal produced an average of 18 articles per year. The first RPA (RPA A), “Enhancing decision making within the agricultural sectors of society,” received the most attention, followed by RPA B, which focused on rural-urban interactions. Within RPA A, the largest number of articles addressed the key research question, “What are the most effective ways to identify and communicate information that has economic and social value?” Under this question, the priority initiative (PI), “Analyze and strengthen the effectiveness of communications content and methods in communicating information,” garnered the most research attention. Findings showed a dearth of studies in PIs across the four RPAs, including economic returns to, and social impacts of, agricultural information; how to engage key interest groups in decision making; models of collaboration, negotiation, and conflict management; use of critical theory in analyzing agriculture and related communications; the interplay between data, information and meaning within stakeholders; information asymmetries and barriers to public participation in decision making; the mechanisms by which information is made available; if and how knowledge gains value; and ethical issues and standards. Results prompted seven suggestions for further research progress and direction.
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
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".