Alleviating poverty: how do we know the scope of the problem and when we have solved it?
Bibliographic record
Abstract
Purpose This paper aims to outline and discuss how to incorporate the stakeholder perspective into performance measurement framework to enhance program effectiveness, accountability and understanding in relation to human development issues. Design/methodology/approach An examination of the literature and a review of best practices was undertaken to identify relevant performance measurements and indicators that could be utilized to measure incremental results and impacts related to poverty reduction strategies. Findings Credible demonstration of policy or program impacts for poverty reduction are dependent on understanding the distinction between inputs, outputs, outcomes and indicators. Moreover, to be trusted by the public, performance reporting on poverty reduction needs to focus more selectively on identifying the key measures of performance and the engagement of key constituents. The intention of this paper is to identify some current best practices and suggest a model with potential indicators, which could be utilized to measure incremental results and impacts in relation to human development issues that we contend is the essential next step if the power and resources of stakeholders are to be harnessed in the fight against poverty while enabling organizations to implement new ways of approaching measurement effectiveness and accountability in a strategic and comprehensive manner. Practical implications The paper advocates that an understanding of performance measurement theory and stakeholder engagement process can enable business leaders to create practical performance measurement frameworks, which in turn will lead to enhanced reporting and accountability for poverty reduction impacts and results. Originality/value This paper presents an overview of the literature which both enhances personal knowledge and understanding at the theoretical and practical levels enabling business leaders to gain insight on the inherent stakeholder factors that need to be considered when designing performance measurement strategies and reporting frameworks.
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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 | 0.046 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.019 | 0.045 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".