Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Data engagement has become an important facet of engaged citizenship. While this is celebrated by those who advocate for expanding participatory channels in civic experience, others have rightfully expressed concern about the complicated dimensions of balancing access with data literacy. If engaged citizenship increasingly requires the ability to interpret civic data through city dashboards and open data portals, then there is a concomitant requirement for diverse populations to develop critical perspectives on data representation (what is commonly referred to as data visualisation, information graphics, etc.). Effective data representations are used to ground conversations, communicate policy ideas and substantiate arguments about important civic issues, but they are also frequently used to deceive and mislead. Expanding statistical, graphical, digital and media literacy is a necessary component of fostering a critical data culture, but who are the beneficiaries of expanded models of literacy and modes of civic engagement? Which communities are invalidated in the design of civic data interfaces? In this article, I summarise the results of a design study undertaken to inform the development of accessible data representation techniques. In this study, I conducted fourteen 2-h participatory design-inspired interview sessions with blind and visually impaired citizens. These sessions, in which I iteratively developed new physical data objects and assessed their interpretability, leveraged a public transit dataset made available by the City of Toronto through its open data portal. While ostensibly “open,” this dataset was initially published in a format that was exclusively visual, excluding blind and visually impaired citizens from engaging with it. What I discovered through the study was that the process of translating 2D, screen-based civic dashboards and data visualisations into tangible objects has the capacity to reintroduce visual biases in ways that data designers may not generally be aware of.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it