Bibliographic record
Abstract
J. Edward Chamberlin’s book, If This Is Your Land, Where Are Your Stories?: Finding Common Ground , contributes to important discussions in Canada about the role of stories in maintaining social divisions, and how stories might help move beyond political conflict if not contradiction and difference. Yet while Chamberlin rightly rejects binary divisions between reality and stories, his framework ultimately collapses one end of the binary (the material) into the other (the imagined). As a result, he downplays distinctions between different types of stories: namely, those that speak from positions of power in the service of social inequality, and those that speak from positions of oppression in the name of social justice. By contrast, this essay argues that a politics of liberation involves the capacity to choose between competing stories. Building on Bakhtinian literary theory to understand the narrative character of human existence as an inseparable part of the materiality of life, the authors discuss the political implications of the fact that stories are always embodied and embedded in a contested and unequal social whole that is in important ways beyond language. Abstract: Le livre de J. Edward Chamberlin intitule If This Is Your Land, Where Are Your Stories?: Finding Common Ground contribue a d’importantes discussions au Canada concernant le role des histoires dans le maintien des divisions sociales et comment ces histoires peuvent nous aider a passer outre au conflit politique sinon aux contradictions et aux differences. Bien que Chamberlin rejette avec raison les divisions binaires entre la realite et les histoires, son cadre conceptuel cause l’affaissement d’un extreme (le materiel) dans l’autre (l’imaginaire). Pour cette raison, il minimise les distinctions entre differents types d’histoires, specialement celles provenant d’une position d’autorite qui portent sur l’inegalite sociale et celles provenant d’une position d’oppression qui portent sur la justice sociale. Le present article avance plutot qu’une politique de liberation integre la capacite de choisir entre des histoires concurrentes. En se servant de la theorie litteraire bakhtinienne pour mieux comprendre le caractere narratif de l’existence humaine en tant qu’element inseparable de la materialite de la vie, les auteurs discutent des implications politiques du fait que les histoires sont toujours incarnees et integrees dans un tout social conteste et inegal qui va, de certaines facons importantes, bien au-dela de la langue.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".