Unintended Behavioural Consequences of Publishing Performance Data: Is More Always Better?
Bibliographic record
Abstract
This paper explores the proposition that IT-driven provision of open data may have unanticipated consequences. Transparency is normally considered desirable: knowledge is “power”, the “oxygen of democracy” and so on. Accordingly there has been a trend towards greater freedom of information, with citizens given access to an increasing diversity of datasets. For many years, governments have produced one particular type of data specifically for public consumption: performance data, such as hospital waiting list statistics, figures on crime, and school performance league tables. Having more information is usually considered beneficial, particularly when there is little available. But when the information supply becomes plentiful, it is not clear that benefits continue to accrue in a simple way. Some apparently negative repercussions are being observed from the publication of performance data. For example, in education the use of league tables seems unable to correct performance problems in some schools, and may even depress performance. Similar effects are observed in other spheres. Data reporting a decreasing threat of crime may be linked with a widespread sense of heightened danger. In the private sector, publication of CEO salaries seems to have fuelled rampant salary inflation. These effects are to do with the cumulative impact of individual behaviours when people respond en masse to information. Individuals react according to their environment, which includes data, creating a complex system with potentially unpredictable and non-intuitive behaviour. We may hope that increased access to data will create net benefits, but evidence suggests that we cannot assume this will always be true. This paper reviews the results of research into this phenomenon from theoretical and empirical perspectives. Results indicate that the publication of performance data can affect the behaviour of service providers, the media, and service consumers, and that the effects are heavily situation-dependent and by no means universally benign. An agenda for further research is outlined, which may help to guide the formulation of policies regarding the publication of government performance data in particular and open data provision in general.
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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 | 0.080 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".