Navigating and Combating “Digital Information Minefields” in our Era of Digital Deceit
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 our post-truth era, it is becoming increasingly difficult for people to deal with fake news, artificial intelligence, increasing algorithms, Internet censorship, and resulting manipulation of digital users. Social media usage and digital technologies are utilized not only in people’s daily lives, but also in educational contexts. In this perplexing political and corporate landscape, a university Education Librarian and Education Professor working in a Faculty of Education have teamed together to examine ways to address this minefield in their case study research involving ninety-one students. Outlined is a collaborative, responsive, pedagogical approach in which critical research skills and educational curricula are delineated and related to creative and participatory educational practices. An emphasis is placed on arts-based inquiry and student imaginative collaboration. This pedagogy enables students to become more critical consumers and skilled producers of knowledge, facilitating student research and communication of well-developed ideas within their own digital and teaching lives.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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