Critical discourse analysis of perspectives on knowledge and the knowledge society within the Sustainable Development Goals
Bibliographic record
Abstract
Abstract Critical discourse analysis (CDA) is employed to analyze discourses of knowledge and the knowledge society in the Sustainable Development Goals (SDGs). Discourse analysis is a collective name for a number of scientific methodologies for analyzing semiosis, namely how meaning is created and communicated though written, vocal or sign language. Employing a genealogical approach which locates discourses in the field of prior discourses, two prior discourses of the knowledge society are identified in the key document of the SDGs. The concepts knowledge and knowledge society are found to have a marginal position within the main policy document “Transforming our world,” adopted by the United Nations in September 2015. The techno‐scientific‐economic discourse is found to be dominant at the level of implementation and of goals, while there is some evidence of the pluralist‐participatory discourse at the level of vision and strategy. Analysis of some of the policy advice provided by international organizations and civil society indicates that more pluralist‐participatory discourses on knowledge were represented when the SDGs were being formulated. Developed countries and the corporate sector were very influential in determining the final text and were probably instrumental in excluding more transformational discourses and maintaining the status quo.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".