The Legitimacy of Global Social Indicators : Reconfiguring Authority, Accountability and Accuracy
Bibliographic record
Abstract
This paper was written in response to an invitation by the organisers of the 2016 Brussels “colloquium on inter-normativity” to address “law-like” or “law related” transnational normative phenomena so as to assess inter alia, “the validity and legitimacy of social indicators such as university rankings, credit rating agencies indicators, rankings of legal regimes and other attempts to provide indicators in areas of social law, human rights law and business law”. The first part discusses the definition of global social indicators, how they relate to other normative materials — from law to algorithms — and what could be meant by asking for them to be legitimate. The second part then examines the implications of debates about and descriptions of indicators for three key aspects of legitimacy : authority, accountability and accuracy. It suggests that indicators are indeed assessed in terms of these criteria but that they also serve to reconfigure and transform them. As noted in the concluding section the paper argues that the relationship between the three aspects of legitimacy identified needs further examination, but that the challenge they pose is more fundamental. Objections to indicators turn on the ideas of the good society they embody, the part they play in constructing the relationship between more or less economically developed societies, and the kind of governance model they reflect — for example their tendency to substitute technicalities for political participation and their link to audit culture. On the other hand, even if caution is needed in endorsing the use of indicators in projects of governance, they do often have advantages of publicness, contestability and openness to violation when compared to the spread of algorithmic regulation.
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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 | 0.088 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".