Critical discourse analysis as a reflection tool for information literacy instruction
Bibliographic record
Abstract
This article uses the theoretical perspectives of critical discourse analysis (Mayr, 2008; Fairclough, 1992) and critical pedagogy (Pagowsky & McElroy, 2016; Accardi, et al., 2010) to explore how language is a socially regulating structure used to represent and maintain power within the academic context. These perspectives are applied to two case studies of library terminology used in the authors’ library orientation sessions to examine how language reinforces Western academic ideologies and structures of power in the information literacy (IL) classroom. This analysis facilitates an exploration of how language used in these contexts can both alienate and empower students within the IL classroom. In addition, other aspects that are explored include power dynamics and student voice within the classroom, critical discourse analysis as a tool for IL instruction reflection, and how these are connected to critical pedagogy. The authors also provide questions regarding privilege and power in IL to support library professionals in fostering meaningful reflections and dialogue, challenging their status quo and exploring new approaches to using critical IL in teaching.
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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 | 0.042 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".