Bibliographic record
Abstract
Abstract Emergent realities such as the COVID‐19 pandemic and corresponding “infodemic,” the resurgence of Black Lives Matter, climate catastrophe, and fake news, misinformation, disinformation, and so on challenge information researchers to reconsider the limitations and potential of the user‐centered paradigm that has guided much library and information studies (LIS) research. In order to engage with these emergent realities, understanding who people are in terms of their social identities, social power, and as epistemic agents—that is, knowers, speakers, listeners, and informants—may provide insight into human information interactions. These are matters of epistemic injustice. Drawing heavily from Miranda Fricker's work Epistemic Injustice: Power & the Ethics of Knowing, I use the concept of epistemic injustice (testimonial, systematic, and hermeneutical injustice) to consider people as epistemic beings rather than “users” in order to potentially illuminate new understandings of the subfields of information behavior and information literacy. Focusing on people as knowers, speakers, listeners, and informants rather than “users” presents an opportunity for information researchers, practitioners, and LIS educators to work in service of the epistemic interests of people and in alignment with liberatory aims.
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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.069 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".