Searching for meaning: Co-constructing ontologies with stakeholders for smarter search engines in agriculture
Bibliographic record
Abstract
A key challenge in agriculture, as in other disciplines, is taking a large body of research-based knowledge and making it meaningful to the user-audience. Computer aided search engines potentially can offer widespread access to large repositories with relevant reports and publications, however the usefulness of such systems for the practitioners who are dealing with multi-faceted and context-related issues is often limited. Building search engines with user-centered ontologies offer a means of resolving this as it provides a vocabulary common to different stakeholders and can optimise the interaction between practitioner users and the expert system.The paper critically reflects on the methodology used to construct a user-centered ontology in the development of a search engine designed to help agricultural practitioners (farmers and advisers) find useful research outputs. This involved the iterative participation of domain experts, adviser practitioners and stakeholder communities in ten diverse case studies across Europe. Specifically it analyses the design, validation and evaluation phases of the ontology development drawing on qualitative data (reports, observations, interviews) from four case studies and asks: How effective is the process of co-constructing an ontology with experts, practitioners and other stakeholders in enabling the search for useful and meaningful knowledge? In doing this, it contributes to a deeper theoretical understanding of shared concepts and meanings in the context of digital communications in the agricultural arena by adapting Carlile’s (2004) framework of syntactic, semantic and pragmatic capacities.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.029 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".