The Political Economy of Information Literacy: A Corpus-Assisted Critical Discourse Analysis of UNESCO’s Media and Information Literacy: Policy Strategy Guidelines
Bibliographic record
Abstract
This dissertation explores the relevance of information literacy to information policy. While information policy makers have been concerned about access to information through ICTs, there has not been a robust consideration for the role of information literacy education in information policy. With the emergence of the term in 1974, information literacy has been discursively linked to ICTs. This study uses a political economy of information lens to examine the discursive relationship between the increasing centrality of ICTs to the global economy and the development of information literacy policy and strategy guidelines at the supranational level. It examines how the production, distribution, and use of information is presented in UNESCO’s 2013 publication Media and Information Literacy: Policy & Strategy Guidelines. Critical discourse analysis and corpus linguistics are employed to analyse how the words information and technology are constituted and reproduced in the document. The study illustrates that the growing global interest in developing both information literacy policy and programming relates to, is influenced by, and mirrors ideological narratives of technologies as forms of empowerment that ultimately embed information literacy in a capitalist mode of production. The study questions the suggestion that media and information literacy are solutions to reduce gender inequality, ensure democratic engagement, and empower people in social decision-making. The concluding chapter demonstrates a disjuncture between the problems and underlying issues the authors suggest media and information literacy education can solve for all people with the social, political, and economic realities in 10 sub-Saharan Africa countries. Autonomist Marxism is used in the concluding chapter to identify and amplify various sub-Saharan African efforts to resist and struggle against a Western imposed digital capitalism. This resistance is demonstrated in how leaders in these countries describe their information needs, limit the use of ICTs in education, and develop their intellectual property laws, particularly laws related to traditional knowledge and traditional cultural expressions. The dissertation encourages librarians and information policy makers to consider and engage with the political economy of information literacy—a definition of which is proposed.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".