Available at:http://ro.uow.edu.au/meme/vol1/iss2/5 Socially Responsible Journalism- The Kerala Model
Bibliographic record
Abstract
Though the Indian diaspora is scattered all over the world, the GCC countries have a remarkably high presence of Indian expatriates. The number of Non Resident Indians or NRIs in the GCC alone is an astounding six million (Shamnad, 2011). Unlike in the US, UK, Canada and other favourite NRI destinations, NRIs in the GCC countries are mainly from the southern Indian states of Kerala, Karnataka, Andhra Pradesh, and Tamil Nadu with Kerala the main contributor. The favoured destination for Keralites in the Arabian Gulf is the UAE. In 2007 42 percent of emigrants from Kerala chose to work in the UAE (Zachariah and Rajan, 2007). Along with the states of Punjab and Gujarat, Kerala has for centuries engaged in trade with far-away lands. In the early 20th century there was a considerable influx into Ceylon and Malaysia. Most of the emigrants were semi-skilled workers who picked up jobs in tea and rubber plantations. In the mid 1970s the Arabian Gulf emerged as a favourite destination for job seekers. Professionals- especially nurses, teachers, doctors and IT specialists- from Kerala have sought occupation in Germany, US, UK and various African countries for decades (Samuel, 2011). The
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.257 | 0.097 |
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".