Bibliographic record
Abstract
Area Studies has always been approached ambivalently since its political birth after the Second World War. Despite a quiet acknowledgement of the contribution of Area Studies in the production of knowledge from the local lens to correct the ‘universal’ Western perspective of the knowledge produced by social sciences, questions are raised about its very existence in the era of globalization. This paper addresses the problematic of the marginalized position of Area Studies. The discussion will include; i) articles by Arif Dirlik, Ravi Arvind Palat, Tessa Morris-Suzuki to address the problematic of marginalization of Area Studies; ii) the articles by Edward Said, Aijaz Ahmad, Dispeh Chakrabrty, Vivek Chibber, and Kuan-Hsing Chen to assess the limits of Postcolonialism and Marxism in deconstructing Eurocentrism of Area Studies; and finally iii) the scholarly debates by Asef Bayat, David Ludden, Neil Smith, Naoki Sakai, Christian von Soest, and Alexander Stroh to discuss the utility of comparative method as a bridge to ford the rifts between Area Studies and social sciences. It is necessary to broaden the scope of Area Studies by engaging in cross-regional as much as cross-disciplinary research with the social sciences and other disciplines which are trying to meet the demands of transnational pressures generated by the global capitalism. The selected scholars highlight the need to revise Area Studies by proposing new approaches to free it from Eurocentrism and to make it more interdisciplinary to meet the demands of globalization
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".