The Dynamics of Opening Government Data A White PaperTHE DYNAMICS OF OPENING GOVERNMENT DATA
Bibliographic record
Abstract
The development of this white paper was made possible with funding from SAP, a provider of enterprise software for the public sector. We would like to thank Peg Kates, Liz McGowan, and Russ LeFevre from SAP for their interest and excitement. We would also like to thank Ashley Casovan, Sanjay Punjabi, Laurie Bowe, and Steve Aguiar from the City of Edmonton and Niels Hansen from Agilite Software for taking the time to tell us the story of the release of street construction projects data. The Center for Technology in Government, University at Albany would also like to thank the government, academic, and industry experts who participated in the Center’s June 26-27, 2012 Open Government Consultative Workshop. Their valuable insights and suggestions on a preliminary draft of this paper were incorporated into the fi nal version. The workshop participants are listed below. Mr. Brian Burke
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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 | 0.069 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.044 | 0.055 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".