Farm households in transition: A case study of agricultural industrialization and agroecological impacts on Prince Edward Island, Canada
Bibliographic record
Abstract
abstract: The history of agricultural industrialization, a complex transition with global and local drivers and effects, is enhanced when local participants in the transition--farm households--contribute to the narrative. This thesis presents an in-depth case study of the household-level motivations and ecological impacts of agriculture during industrialization in Prince Edward Island (PEI), Canada, c. 1960s-present. After a review of the theoretical frameworks for agricultural change studies, the historical context of PEI’s agricultural industrialization and the province-wide ecological effects are analyzed by interpreting historical, scientific, and grey literature. Then, a discussion of farm households’ role in connecting large-scale (often exogenous) factors with small-scale factors provides the background to the novel study, “The Back 50 Project”. Using a public participatory historical GIS (PPHGIS) online survey, this study invited PEI’s agricultural community to use historical maps to describe the agricultural land use change (ALUC) they have engaged in and observed since the start of industrialization. This study found that the strongest motivations for ALUC were proximate causes—namely, households’ resources and goals—rather than high-level historical drivers. The reported agroecological effects tended to concern on-farm ecosystems more than off-farm ecosystems, and they ranged in their harm or benefit, with harmful impacts following the historical contexts. Finally, the synthesis of these historical and ecological contexts with this household-level study aims to create a holistic narrative of PEI’s agricultural change over the past fifty years and provide recommendations for PEI’s future sustainable agricultural development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".