Disruptive Technologies in the Agri-food Sector: A Knowledge Synthesis
Bibliographic record
Abstract
The agri-food sector in Canada is entering an “age of disruption” where the rapid expansion of technology, like IoT, genetic advancements, and robotics, has potential to fundamentally reshape the futureof work and the development of rural communities across Canada. This presentation reports on the firstphase of a multi- phased project that assesses the impacts of disruptive technologies in the agri-food sector, including the scope of technologies that could disrupt traditional production practices and the future of work. An overview of the project and the findings from our initial research and knowledgesynthesis is presented along with the planned future phases and next steps. Using technology assessment,key informant interviews and comparative case studies, this research aims to identify disruptive technologies and companies, assess their social and spatial implications, and explore how regional stakeholders are responding to these impacts. This research aims to assist local and provincial policymakers in designing and assessing new policies and programs to respond to the impacts of disruptive technologies in the agri-food sector.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".