Bibliographic record
Abstract
It is important to align what we teach with how we teach information literacy (IL), otherwise we may inadvertently engage with what Hipple et al. (2021) identify as the pedagogy of hypocrisy through neoliberal pressure in higher education. This occurs when there is a misalignment between the values and principles behind what we teach and the pedagogical approaches we take when teaching. For example, when teaching IL concepts that intend to engage with social justice themes around access privilege and information, the pedagogy of hypocrisy can occur when we simply demonstrate how to access library resources on the library’s website, without engaging in critical conversations about systems that contribute to inequities in access within society. To counter this, Hipple et al. (2021) suggest that those who teach must critically reflect on who they teach for, examine how they use and activate (or co-opt) social justice language, and name dominant and oppressive structures. This paper builds on Hipple's argument to suggest ways of recognising the pedagogy of hypocrisy within IL practices, and argues that this recognition is key to countering hegemonic ideologies within LIS teaching.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".