Aesthetic social representations and concrete dialogues across boundaries: Toward intergenerational CHARACTERization
Bibliographic record
Abstract
In this paper, I present Bertoldo and Castro’s (2019) epistemological limits in relation to Moscovici’s and propose to develop some of Moscovici’s dynamic aspects. Because Bertoldo and Castro (2019) refer to Boulanger and Christensen’s (2018) work—which aims to schematize the representation processes at different levels of abstraction and develop an aesthetic theory of social representations with respect to Simmel—as a dialogical response, I use this same framework to criticize and push further their effort of extending SRT with regard to the subjective dimension. Bakhtin’s work will be partially used to support my arguments and establish the basis for a dialogical model of aesthetic representation as CHARACTERization. I will quickly illustrate the theoretical propositions regarding the analysis of intergenerational practices in Quebec (Canada), thereby introducing the concept of intergenerational CHARACTERization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".