Realising distributed leadership through measurement for change
Bibliographic record
Abstract
Through a systematic reflection on the journey that transformed traditional state-run baby homes in Tajikistan from closed institutions into community-oriented Family and Child Support Centres (FCSC) we reveal key moments of change. This review describes how community consultation with local participants in a development project shifted responsibility and accountability from international to local ownership and how distributed leadership contributes to the decolonisation of social services. Based on these interviews we ask, 'How do the innovations of a social development project become a fixed part of normal local social, cultural and political life; and, how do we know when a new normal is self-sustaining at a local level?' This analysis builds on a network-mapping tool previously described in this journal. Our interviews show that each participant has taken a non-linear journey, building on the networks previously described, under the influence of activities and discussions that emerged throughout the project. We consider how a monitoring, evaluation, and learning process should be responsive over time to these influences, rather than be set at the start of the project. Using the themes that emerge from participants' journeys, we apply a 'measurement for change' (M4C) approach that integrates Monitoring, Evaluation and Learning (MEL) into decision-making. The journey framework applied represents a systematic application of the M4C approach that gives us insight into where local ownership is responsible for the sustainable management of the intervention, and where continued partnership will further strengthen impact and accountability. The exercise has provided evidence of progress towards decolonisation and of the centring of local priorities in MEL and implementation processes.
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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 | 0.123 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".