Equity, Scalability, and Sustainability of Data Science Infrastructure
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
We seek to understand the current state of equity, scalability, and sustainability of data science education infrastructure in both the U.S. and Canada. Our analysis of the technological, funding, and organizational structure of four types of institutions shows an increasing divergence in the ability of universities across the United States to provide students with accessible data science education infrastructure, primarily JupyterHub. We observe that generally liberal arts colleges, community colleges, and other institutions with limited IT staff and experience have greater difficulty setting up and maintaining JupyterHub, compared to well-funded private institutions or large public research universities with a deep technical bench of IT staff. However, by leveraging existing public-private partnerships and the experience of Canada's national JupyterHub (Syzygy), the U.S. has an opportunity to provide a wider range of institutions and students access to JupyterHub.
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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.026 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.015 | 0.023 |
| 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