Bibliographic record
Abstract
IntroductionIn Indian megacities, municipal solid waste management has become a challenging problem, especially in cities like Chennai, which generates 0.71 kg of municipal solid waste per capita every day-the highest in the country (Chennai's per capita waste at 0.7 kg highest in the country, 2014). Growing waste generation is mainly due to population growth, economic development and changing lifestyles. Primarily responsible for waste management, municipalities and local agencies have been ineffective in tackling the waste problem. Some issues related to municipal solid waste management are low priority for safe disposal, lack of appropriate organization, insufficient financial and technical resources, a limited number of disposal sites and inadequate knowledge of disposal methodology (Pandian, Ramanathan and Rawat, 2010: 199).Municipal solid waste is simply collected, transported and dumped without treatment or processing. A substantial amount of waste remains unattended at collection centers, roadsides and riverbanks. Most often cows and other stray animals feed on waste dumped in these places. Open dumping of garbage facilitates the breeding of disease vectors and unsanitary dumpsites increase the risk of groundwater contamination (Kumar, Venkata and Rao, 2013: 48). Asubstantial amount ofthe municipal waste budget (around 75 per cent) is spent on street sweeping, with only 20 per cent on transportation and 5 per cent or less on disposal (Hanrahan, Srivastava and Ramakrishna, 2006: 30). However, in spite of street sweeping, roads remain dirty, spoiling the aesthetic beauty of cities and towns. It is common to see people throw their rubbish onto the street. The prevailing thought is cleaning up is always somebody else's responsibility. The Government of India issued Municipal Solid Waste (Management and Handling) Rules, 2000 to improve waste management. The rules promised environmental sustainability in solid waste management by promoting waste separation, recycling, and use of disposal techniques such as composting and incineration. Government agencies believed privatization to be the panacea for solid waste problems. Under the impetus of Municipal Solid Waste Rules and the privatization drive, Chennai became first city to contract out municipal solid waste management services to a foreign agency, the French company Onyx (Ahluwalia, Kanbur and Mohanty, 2014: 225); however, in spite of the Municipal Solid Waste Rules, the Greater Chennai Corporation1 and the private operators continued to dispose of the collected mixed waste at open dumpsites, posing ongoing risks to the environment and public health (CMDA, 2008).Besides Greater Chennai Corporation and private operators, non-government organizations such as Exnora are also working as conservancy agencies2 3 in the field of solid waste management. Exnora introduced the concept of people participation in solid waste management by forming community-based organizations such as Civic Exnora to independently manage waste in their locality, with the parent body playing an advisory role (Dhamija, 2006: 84). The Civic Exnora innovation has shown that the active participation of people can bring a spark of change. Exnora is active in Chennai, Panipat, Lucknow and Hyderabad, but this paper mainly focuses on Exnora in Chennai. A non-government organization, Civic Exnora provides sustainable waste management systems that are successful in reducing waste and generating employment and income. The community intervention Exnora, however, has a marginal presence and is vulnerable to indiscriminate privatization drives. With this background, this article seeks to understand how Exnora functions and to explore whether Civic Exnora has and to what extent could in the future keep the streets free from garbage and assist rag pickers in spite of the challenges from political pressures and growing privatization.A review of existing literature reveals that several studies on Exnora have been undertaken. …
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".