Bibliographic record
Abstract
When nearly everybody is hooked into a digital world in which misinformation, disinformation, and unchecked bias run rampant, it is crucial for librarians and other information literacy educators to promote critical engagement with the digital in their teaching practices. Therefore, this paper explores various feminist information practices as strong foundations for good critical digital literacy, such as the critical examination of dominant power structures, the use of local and personal knowledge in information-gathering, and recognizing how the digital and real worlds are two inherently interconnected, rather than separate, spheres. These practices meld in the figure of the cyborg librarian. Politically aware and able to move between the digital and real effortlessly, the cyborg librarian, though imperfect, is a useful metaphor for the information and digital literacy instructor to work with as they promote a critical awareness of the rigid hierarchies of power that structure many information-gathering resources. As information literacy educators, we should aspire to become the cyborg librarian in order to more effectively teach critical digital and information literacy to our often very-online students.
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 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".