Alleviating poverty: how do we know the scope of the problem and when we have solved it?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it