Bibliographic record
Abstract
The past several decades has witnessed a marked transformation of Ontario’s agricultural extension services from being a provincial government run service to one that’s is marked by a diverse network of public and private information service providers. This presentation reports a preliminary analysis of the contemporarysoil advisory services, especially looking at how different actors are organised, methods used and emerging challenges. The data were collected using key informant interviews with a purposively selectedadvisor. The findings highlight a variety of service providers at play within the soil advisory services which comprised of numerous commodity marketing boards, producer organisations, input and equipmentsuppliers, OMAFRA and the University of Guelph. These actors are organised through various formal andinformal networks, although respondents expressed their concerns on coordination and communication within the network, with an attendant negative impact on network efficiency in advisory service provision. Although most respondents yearn for an individual approach, mainly represented by the public sector, the current service delivery is dominated by groups and mass methods. The findings also indicate a move fromadvisory roles as generalist knowledge brokers to specialist knowledge brokers. The advisory service isgoverned by a ‘hands off’ approach because of withdrawal of public service support that creates significant gaps for coordination and collaboration among different players. The gaps need to be filled in—eitherchampioned by public or private sector actors—but it is not possible without significant changes in existingpolicy and public supports.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".