Bibliographic record
Abstract
Many agricultural economics researchers want their work to be relevant for policy advice. Governments make efforts to facilitate the use of research in the development of policy advice. This address reviews some of the literature on the policy relevance of research, with a focus on agricultural economics research. Based on the literature, the address identifies 20 elements that researchers may want to consider when seeking to make their research relevant for policy advice. The importance of each of the elements is highlighted by using them to examine the views expressed by 41 analysts and policy advisors in six symposium settings organized over five years on the topic of “What makes agricultural economics research relevant for policy advice?” The key elements for researchers include understanding the policy‐making process, communicating effectively with the right audiences, doing high‐quality work, and paying attention to timeliness and windows of opportunity. The application of the elements is illustrated by several examples of policy‐relevant research. The address suggests that the Canadian Agricultural Economics Society (CAES) seek to increase the valuation of policy relevance in research funding and academia and outlines steps the CAES could take to better enable individual members to increase the policy relevance of their research. De nombreux chercheurs et chercheuses en agroéconomie souhaitent que leurs travaux de recherche puissent influencer l’élaboration des politiques agroalimentaires. Les gouvernements s’efforcent de faciliter l’utilisation des résultats de recherche dans le processus menant à l’élaboration des politiques. Dans le présent discours, je passe en revue quelques travaux sur l’importance de la recherche, en particulier la recherche en agroéconomie, comme élément d’information pour appuyer la réflexion des décideurs. D’après la littérature sur le sujet, il existe vingt éléments que les chercheurs et chercheuses peuvent prendre en considération lorsqu’ils tentent d’augmenter l’influence de leurs travaux dans l’élaboration des politiques. Pour mettre en relief l’importance de chacun de ces éléments, ils ont été utilisés pour examiner les points de vue de 41 analystes et conseillers en politiques qui ont participéà six symposiums au cours des cinq dernières années sur la question « What makes agricultural economics research relevant for policy advice? (Qu’est‐ce qui rend la recherche en agroéconomie pertinente pour l’élaboration des politiques?). Pour les chercheurs, les éléments clés sont les suivants: comprendre le processus d’élaboration de politiques, communiquer efficacement avec les bons auditoires, effectuer des travaux de recherche de qualité supérieure et accorder une attention particulière à la présentation en temps opportun et aux occasions favorables. L’application des éléments est illustrée à l’aide de plusieurs exemples de travaux de recherche pertinent pour l’élaboration des politiques. Le présent discours préconise que la Société canadienne d’agroéconomie (SCAE) tente d’accroître la valeur de la recherche en milieu universitaire et le financement de la recherche pertinente pour l’élaboration des politiques et présente des moyens que la SCAE pourrait mettre en œuvre afin de permettre à ses membres d’avoir un plus grand impact sur l’élaboration des politiques.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".