Growing local food: charting meaning emergence through the dynamics of discourse, rhetoric and framing
Bibliographic record
Abstract
This dissertation seeks to understand how new meanings emerge in the context of institutional change. Existing research seeking to understand shifts in meaning has primarily accessed meaning, across numerous contexts, via the three key constructs of discourse, rhetoric, or framing. Within the context of the emergence of the local food movement in Canada, I employ a mixed methods approach using term frequencies, topic modelling and qualitative content analysis, within a computational grounded theory framework for Big Data analysis. My data consists of all articles containing any mention of the term “local food” in popular Canadian press over 37 years from 1978-2014, a database totalling 31,421 articles. My results show that firstly, new meanings pertaining to local food emerged rapidly over the 37-year period. The emergence of a new meaning for local food, associated with the politicization of food production occurred in the second half of my dataset, whereas the first half was marked by connotations of poverty and hunger, associated with the local food bank. Secondly, unexpected actors were found to significantly impact the propulsion of meaning change, by establishing new vocabularies surrounding the term “local food”. Finally, this dissertation shows that the new meanings associated with local food emerged as a result of discursive opportunities, momentarily arising through the confluence of discourse, rhetoric and framing. I propose an emergent process model of meaning change and, further, propose that discursive opportunity structures can be better understood through the metaphor of an emergent property.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".