How impact measurement devices act: the performativity of theory of change, SROI and dashboards
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 study aims to examine the role of devices in assessing the social impact of an organization. The study examines the effects of device and analyst expertise on the contents and conclusions of the report. Design/methodology/approach Six impact reports based on the same data from the same organization were compared to each other, to the charity data and to the devices used. Specific attention is paid to the role of the device’s sociomaterial form and discursive entanglements. Findings The six reports assessed the impact differently from each other and in ways that were consistent with the devices used. The devices performatively reconfigured the charity in impact reports through a series of omissions and misrepresentations which could be traced to the discourses hardwired into the devices themselves. The devices did not simply present the same impact assessment to different audiences or for different purposes, but (mis)represented the charity in specific ways aligned with the discursive entanglements. Research limitations/implications The performativity of sociomaterial impact devices has implications for how researchers approach the study of impact measurement. Practical implications In this study, faithful adherence to an impact device led to greater omissions and misrepresentations than less expert impact assessments. Analysts should be supported to identify biases in their devices and be aware of sorts of omissions and misrepresentations that may result. Faithful adherence may not be the mark of rigorous analysis. Originality/value Performativity of impact measurement devices is explored with a unique data set.
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 | 0.027 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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