Bibliographic record
Abstract
That the offshore program has remained in place for decades and public outcry against it has been minimal is partly the achievement of a carefully spun discourse of foreign farm labor, as we saw in the previous chapter. In this chapter, I examine how this discourse makes use of several strategies. First, it situates the foreign migrant workers in the context of the familiar landscape of rural Ontario. Second, it frames the representation of offshore labor in dualisms of belonging and nonbelonging. Third, it associates various dualisms with different geographical scales. These scale-particular representations enable seemingly contradictory narratives to coexist. However, in the context of the wider discourse, geographical scales and associated dualisms interlock in a manner that situates seasonal migrant labor in subordinate economic and marginal social roles. It is still common among scholars to use essentialized ethnic categories to assess rural landscapes and examine social relationships in agricultural production. In view of such scholarly practices, it is particularly important to expose the ideological underpinnings of landscape representation. Geography has offered many approaches, associated with different traditions of scholarship, to the study of landscape. These approaches variously treat landscape as an expression of rural lifestyle, a manifestation of everyday social space, a material reflection of social relations, and an ideology. I assume the fourth perspective on landscape, which George L. Henderson (2003) also describes as “apocryphal” landscape because it reveals, not authentic social relations, but ideological ways of seeing. When Stephen Daniels and Denis Cosgrove (1988: 1) say, “A landscape is a cultural image,” they refer to the ideological representation of people and objects through landscape. According to this approach to landscape, the manner in which people are situated and represented in landscape can reveal ideologies of subordination and exclusion. For example, the portrayal of Gypsies as uncivilized, dirty, and a “polluting presence” in the English countryside reflects “the assumption that the countryside belongs to the privileged” (Sibley 1995: 107). In this context, landscape is the discursive construction of “a stereotyped pure space which cannot accommodate difference” (108).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".