Rethinking the Work of Geographical Indications in Asia: Addressing Hidden Geographies of Gendered Labour
Bibliographic record
Abstract
Geographical Indications (GIs) were established as a distinctive category of intellectual property (IP) in the 1994 Trade-Related Aspects of Intellectual Property (TRIPS) Agreement. Reflecting this broad field of legal vehicles, marks indicating conditions of origin (MICOs) denote indications of source, appellations of origin, denominations of origin, and collective trademarks and certification marks. This chapter recognizes the limitations of GIs, examining a need for greater empirical study of the regulations governing their use. Most academic scholarship fails to investigate the social relations between labourers, landowners, producers, and GI institutions, or political movements for decolonization. This chapter, therefore, suggests that local GI governance must be analyzed within a broader scope that encompasses class, ethnicity, and gender. We begin by exploring Darjeeling Tea as a famous form of national GI in India that reflects some of the ideological tendencies of MICO systems. We then demonstrate the global significance of MICO governance by providing insight into contemporary political ecology in Southeast Asia. By examining palm oil expansion, the decline in swidden agriculture, and subsequent environmental and cultural effects, we outline a crisis of biocultural diversity in the region. Lastly, this chapter inquires into the future of MICOs, looking specifically at emerging practices that might encourage alternative value systems to address both environmental and social justice concerns, including greater gender equity. Ultimately, this chapter illustrates how current uses of MICOs uphold entrenched social hierarchies and therefore must be reoriented towards rights-based development and social justice objectives.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".