Social Science or Social Control: Qualitative Researchers’ Dilemma in Contrastive Rhetoric
Bibliographic record
Abstract
As many critics have pointed out, social science is not and has never been a neutral enquiry into human behaviors and institutions. It is strongly implicated in the project of social control, whether by the state or by other agencies, which ultimately serve the interests of a dominant group. In this paper, I will focus on contrastive rhetoric—an area of study in second language writing—as an example of social science research. I will first discuss how, in the field of contrastive rhetoric, a particular “social problem” is first identified and then contained through the collective pronouncements of expert discourse. I will then speculate on the epistemological assumptions of contrastive rhetoric that determine the research focus and influence research findings, which, in turn, can affect the representations as well as experiential realities of the researched groups. Finally, I will discuss how alternative ways of approaching the “problem” could challenge existing paradigms that disadvantage the researched groups.
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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 | 0.678 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.019 | 0.135 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".