Publicness in Governance Initiatives-Bridging the Divide Digitally through Socio-technological Publicness Model for Development
Bibliographic record
Abstract
The term 'governance ’has become a buzzword in the public domain. Governance seems to be more of use of political authorities for control, than the management of resources for socio-economic development at large. There are conflicts of stakes, as each stake claims to serve the interests of the 'intended ’but, actually, might not be doing the same, rather engaged in achieving one's own agenda. It is generally very difficult to get the interest of common citizens, especially the poor and marginalized, served through direct physical participation. The representations are taken and ensured but experience suggests that there are always 'questions ’over the representations 'being true'. This results in lack of 'publicness ’in development initiatives and maximization of the benefits to 'other stakeholders ’at the cost of 'intended beneficiaries'. But with the advent of e-culture, especially egovernance frameworks, a ray of hope has been seen. The scope of e-governance being vast and with the advancements in Information Communication and Technologies, one could explore plenty of possibilities to explore a citizen-centric framework for governance. In this paper, a model titled as 'Socio-technological Publicness Model for Development ’has been proposed and discussed under Rural e-Governance Framework. The model would help in ensuring citizens ’participation from the planning to the decision making processes of programmes and policies, with the purpose to fill the gap between their expectations in relation to their actual needs and available development options. The model relies on the utilizations of existing infrastructure, institutional set-ups and resources at Panchayats and at the district. The model also helps in creating a culture of transparency and accountability at various levels of governance institutions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".