Towards Strengthening Technology Culture amongst MSEs: Evidence from a Field Study in Bangalore, India
Bibliographic record
Abstract
The Micro and Small Enterprises (MSEs) contribute a great deal in income and employment generation in India. The MSEs competitiveness is often marred by the level of technology they adopt. Realizing the vital role played by these units in the economy, previous Government of India provided tacit support to encourage them in adopting new technology. This shifted away from the hitherto approach of viewing the technology as the mere generators of employment. While state intervention could harness their growth, the forms of intervention require a careful examination of technology culture amongst MSEs. To explore firms’ tendencies in this regard, a field level investigation was conducted covering a few auto component manufacturing firms based in Bangalore. Availability of skilled labor and ease of finance were found to be the major constraints to technology acquisition and upgradation. While there are a plethora of government sponsored schemes implemented through other formal credit market channels, there are obstacles faced by the units in accessing those credit facilities. Based on the findings, the paper outlines some state interventions that may enhance technology acquisition and upgradation processes amongst the MSEs
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".