Shifting Discourses in Social Sciences: Nexus of Knowledge and Power
Bibliographic record
Abstract
Developing societies have often relied on Western or Eurocentric knowledge as a consequence of colonization process, intellectual imperialism, as well as experiences of the forces of modernization and its dependencies, as well as globalization rhetoric. The basic premise of this article is that all civilizations have potential sources of social science theorizing. This article explicates the general development of social sciences discourses in the West and response from the developing societies in particular. Specifically the article attempts to elaborate on the impasse in social science as it relates to domination of Eurocentric knowledge and the marginalization of local knowledge. In framing this article, insights on captive mind by Alatas and the notion of modern power by Foucault are utilized. Our approach to power and knowledge involves a multi-scalar analysis of the functioning of power relations that impinged on the social science discipline as experienced in the developing societies. Contestations from within the Western realm as well as developing societies appear to provide a remedy to the contemporary situation, but somewhat at a slow pace and operating in the margins. The relevance of adapting to local context and the institutional capacity for self-empowerment in localizing knowledge is a necessary path that developing societies should take.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.136 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".