Humanizing Data: A framework for Open Government Data decision making
Bibliographic record
Abstract
The objectives of Open Government Data (OGD) are to promote transparency, accountability, and collaboration with the public. Structural issues within Subnational OGD and limited governance of urban data collection technologies have led to public concerns about ethical data collection, privacy, and digital human rights. However, OGD research is often purpose-driven evaluating specific parts of the system from a technical perspective and forgoes what data means to us as humans living in cities. The purpose of this study is to examine OGD use in Canada’s cities from a values-based perspective. Using design thinking, strategic foresight, and systems thinking methodology, this research first investigates the current state of the system and uncovers that the metaphor, knowledge is power, is contributing to its insufficiencies. Alternative system metaphors are unpacked using future scenarios that demonstrate areas of critical uncertainty to which we are unprepared. Using the scenarios as the guide, this research submits decision making principles that OGD decision makers and open data advocates can use to humanize data.
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.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.052 | 0.079 |
| Research integrity | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".