Bibliographic record
Abstract
The global institutional changes initiated in 1995 and implemented in 2005 impacted India's pharmaceutical and textile industries, both of which were of immense strategic importance to the Indian economy. To sustain these industries and improve their competitiveness under new institutional regimes, successive Indian governments have initiated numerous policies that continue to evolve. The purpose of this chapter is to outline the industry-specific policy initiatives promulgated to cope with global institutional changes. However, to understand the underlying logics and the various components of the policy choices, one has to look at the national institutional context within which these industries historically evolved prior to 1995, as the respective trajectory of each industry in the previous periods impacted the policy constraints and opportunities in dealing with global institutional changes. Accordingly, before detailing the post-1995 policy initiatives, this chapter first discusses the historical context of these industries, especially in relation to policy regimes. Given their importance to the Indian economy, each of these industries has been extensively studied by numerous scholars, and these studies have critically evaluated numerous policy interventions over the years. The purpose of this chapter is to highlight the broad and evolving thrust of policy choices in a comparative framework rather than attempt to be comprehensive in detailing all policy interventions before or after global institutional changes impact each industry. The chapter divides the policy regimes into four periods: (a) import substitution industrialisation, 1947–1985; (b) economic liberalisation, 1985–1995; (c) preparing for global institutional change, 1995–2005; and (d) coping with global institutional change, 2005–2020. After discussing the evolving policy choices in each of these periods regarding their applicability to the two industries, these choices are placed within an appropriate industrial policy framework. This framework will then guide analyses in subsequent chapters in terms of the impact of policy interventions on the evolution of the two industries and the implications of firm strategic choices within each industry in response to the two global institutional changes.
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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 | 0.001 | 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) | 0.311 | 0.010 |
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; both teacher heads agree on what is shown here.
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".