Supporting the development of critical data literacies in higher education: building blocks for fair data cultures in society
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
In the last ten years digitalized data have permeated our lives in a massive way. Beyond the internet ubiquity and cultural change outlined in what Castells (1996) called the network society, we are now witnessing a datafied society, where large amounts of digital data¿the DNA of information¿are driving new social practices. The most enthusiastic discourses on this abundance of data have emphasized the opportunity to generate new business models, with professional landscapes connected to data science and open practices in science and the public space (EMC Education Services 2015; Scott 2014). However, more recently, the rather naïve logic of data capture and its articulation through various algorithms as drivers of more economical and objective social practices have been the object of criticism and deconstruction (Kitchin 2014; Zuboff 2019). The university as an institution fell into this paradigm somehow abruptly, while striving to survive its crisis of credibility. The digitalization of processes and services was considered a form of innovation and laid the foundations for the later phenomenon of datafication (Williamson 2018).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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