Bibliographic record
Abstract
This paper aims to report on how accounting practices are framed and implicated in experiential learning, adaptation and change processes at the micro level in one of the biggest publicly delivered social services program in India. The paper draws on Kolb (1984) and Lewin (1943) four stage experiential learning Model (ELM) to examine the role accounting plays in processes of learning, knowledge and adaptations. An attempt is made to theorise learning and adaptation that can be triggered and supported by accounting practices. Accounting processes gather meaning based on the manner in which they are enlisted in contextual and cultural environments. They are known to be not only be the change but also be the provider of the preconditions for change. In contrast to a negative enlistment of accounting in developing nations settings (Rahaman, Everett & Neu, 2007), this study argues that the accounting and accountability practices in MGNREGS create conditions for newer understandings, learning and change by providing information and concrete experiences around which communication, interactions and reflections take place and as a platform for testing, sense-making and knowledge assimilation in developing nation village settings. In effect, these accounting practices have the potential to positively shape not only organizational processes but the socio-economic life of the wider social at the micro level. Social services is an underexplored area in accounting literature and especially so in developing nation contexts. This paper introduces a social accounting perspective on learning and change at the micro citizen level in the field of social services. Also, it introduces the Experiential Learning Theory (ELT) and Kolb’s ELM as an alternative accounting framework, which has so far only been used for research in accounting education.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".