DataGM: Curating an Environment for Change
Bibliographic record
Abstract
Open Data Cities is an ongoing investigation into how a city may move towards adopting, in specific terms, an open data framework, and, in general terms, openness. It is an experiment in participatory policy and infrastructure, and in curating an environment for change. Uniquely, our focus is on the entire ecosystem at once, and developing an ecology around open data to create sustainable impact. One dimension of this infrastructure is DataGM which, as an output of the Open Data Cities research, and artefact or ‘Open Digital Resource’, is the focus of this paper. DataGM used a process of participatory policy and action learning in the Greater Manchester city region. We engaged policy makers from 10 local authorities, data managers from agencies including Transport for Greater Manchester, digital businesses, and supported a grass-roots developer community. Our development approach drew significantly on Actor Network Theory (ANT). According to ANT, the on–going processes of “translation” are key sources of social order. “Translation” generates ordering effects, such as organisations, institutions, devices, and agents. Each of these have their own “resistances”, and social change, as evidenced by Data GM, is very much about a struggle of reorganising the resources and relations in the ‘actor–network’. This paper presents an analysis of the practical application of this theory to our problem domain and, reflecting on our experience, makes recommendations for participatory policy and infrastructure intervention at a city scale.
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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 | 0.032 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".